Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. A recent comparison of genomics with social media, online videos and other data-intensive disciplines suggests that genomics alone will equal or surpass other fields in data generation and analysis within the next decade . The voldeep learning has come to refer to a collection of new techniques that, together, have demonstrated breakthrough gains over existing best-in-class machine learning algorithms across several fields. For example, over the past 5 years, these methods have revolutionized image classification and speech recognition due to their flexibility and high accuracy [The term accuracy . More reaccuracy , computaaccuracy , dermatoaccuracy . Across Deep learning approaches grew from research on artificial neurons, which were first proposed in 1943  as a modsupervised applications—where the goal is to accurately predict one or more labels or outcomes associated with each data point—in the place of regression approaches, as well as in unsupervised, or ‘exploratory’ applications—where the goal is to summarize, explain or identify interesting patterns in a dataset—as a form of clustering. Deep learning methods may, in fact, combine both of these steps. When sufficient data are available and labelled, these methods construct features tuned to a specific problem and combine those features into a predictor. In fact, if the dataset is ‘labelled’ with binary classes, a simple neural network with no hidden layers and no cycles between units is equivalent to logistic regression if the output layer is a sigmoid (logistic) function of the input layer. Similarly, for continuous outcomes, linear regression can be seen as a single-layer neural network. Thus, in some ways, supervised deep learning approaches can be seen as an extension of regression models that allow for greater flexibility and are especially well suited for modelling nonlinear relationships among the input features. Recently, hardware improvements and very large training datasets have allowed these deep learning techniques to surpass other machine learning algorithms for many problems. In a famous and early example, scientists from Google demonstrated that a neural network ‘discovered’ that cats, faces and pedestrians were important components of online videos [Deep learning does many of the same things as more familiar machine learning approaches. In particular, deep learning approaches can be used both in e videos  without Several important advances make the current surge of work done in this area possible. Easy-to-use software packages have brought the techniques of the field out of the specialist's toolkit to a broad community of computational scientists. Additionally, new techniques for fast training have enabled their application to larger datasets . Dropoutet al. [et al. [This review discusses recent work in the biomedical domain, and most successful applications select neural network architectures that are well suited to the problem at hand. We sketch out a few simple example architectures in et al.  covers n [et al.  provide While deep learning shows increased flexibility over other machine learning approaches, as seen in the remainder of this review, it requires large training sets in order to fit the hidden layers, as well as accurate labels for the supervised learning applications. For these reasons, deep learning has recently become popular in some areas of biology and medicine, while having lower adoption in other areas. At the same time, this highlights the potentially even larger role that it may play in future research, given the increases in data in all biomedical fields. It is also important to see it as a branch of machine learning and acknowledge that it has the same limitations as other approaches in that field. In particular, the results are still dependent on the underlying study design and the usual caveats of correlation versus causation still apply—a more precise answer is only better than a less precise one if it answers the correct question.1.1.With this review, we ask the question: what is needed for deep learning to transform how we categorize, study and treat individuals to maintain or restore health? We choose a high bar for ‘transform’. Grove , the forThere are already a number of reviews focused on applications of deep learning in biology –17, heal1.1.1.A key challenge in biomedicine is the accurate classification of diseases and disease subtypes. In oncology, current ‘gold standard’ approaches include histology, which requires interpretation by experts, or assessment of molecular markers such as cell surface receptors or gene expression. One example is the PAM50 approach to classifying breast cancer where the expression of 50 marker genes divides breast cancer patients into four subtypes. Substantial heterogeneity still remains within these four subtypes ,25. Give1.1.2.Deep learning can be applied to answer more fundamental biological questions; it is especially suited to leveraging large amounts of data from high-throughput ‘omics’ studies. One classic biological problem where machine learning, and now deep learning, has been extensively applied is molecular target prediction. For example, deep recurrent neural networks (RNNs) have been used to predict gene targets of microRNAs (miRNAs) , and CNN1.1.3.Although the application of deep learning to patient treatment is just beginning, we expect new methods to recommend patient treatments, predict treatment outcomes and guide the development of new therapies. One type of effort in this area aims to identify drug targets and interactions or predict drug response. Another uses deep learning on protein structures to predict drug interactions and drug bioactivity . Drug re2.ad hoc, potentially impeding the identification of underlying biological mechanisms and their corresponding treatment interventions.In healthcare, individuals are diagnosed with a disease or condition based on symptoms, the results of certain diagnostic tests, or other factors. Once diagnosed with a disease, an individual might be assigned a stage based on another set of human-defined rules. While these rules are refined over time, the process is evolutionary and ad hoc historical definitions of disease. Perhaps deep neural networks, by reevaluating data without the context of our assumptions, can reveal novel classes of treatable conditions.Deep learning methods applied to a large corpus of patient phenotypes may provide a meaningful and more data-driven approach to patient categorization. For example, they may identify new shared mechanisms that would otherwise be obscured due to In spite of such optimism, the ability of deep learning models to indiscriminately extract predictive signals must also be assessed and operationalized with care. Imagine a deep neural network is provided with clinical test results gleaned from EHRs. Because physicians may order certain tests based on their suspected diagnosis, a deep neural network may learn to ‘diagnose’ patients simply based on the tests that are ordered. For some objective functions, such as predicting an International Classification of Diseases (ICD) code, this may offer good performance even though it does not provide insight into the underlying disease beyond physician activity. This challenge is not unique to deep learning approaches; however, it is important for practitioners to be aware of these challenges and the possibility in this domain of constructing highly predictive classifiers of questionable utility.Our goal in this section is to assess the extent to which deep learning is already contributing to the discovery of novel categories. Where it is not, we focus on barriers to achieving these goals. We also highlight approaches that researchers are taking to address challenges within the field, particularly with regards to data availability and labelling.2.1.Deep learning methods have transformed the analysis of natural images and video, and similar examples are beginning to emerge with medical images. Deep learning has been used to classify lesions and nodules; localize organs, regions, landmarks and lesions; segment organs, organ substructures and lesions; retrieve images based on content; generate and enhance images; and combine images with clinical reports ,40.Though there are many commonalities with the analysis of natural images, there are also key differences. In all cases that we examined, fewer than one million images were available for training, and datasets are often many orders of magnitude smaller than collections of natural images. Researchers have developed subtask-specific strategies to address this challenge.Data augmentation provides an effective strategy for working with small training sets. The practice is exemplified by a series of papers that analyse images from mammographies –45. To eet al. [A second strategy repurposes features extracted from natural images by deep learning models, such as ImageNet , for newet al.  repurposet al. ,52 and net al. ,54 as weet al. . Pre-traet al. . Reusinget al. –59. A deet al. . Howeveret al. [The technique of reusing features from a different task falls into the broader area of transfer learning (see Discussion). Though we have mentioned numerous successes for the transfer of natural image features to new tasks, we expect that a lower proportion of negative results have been published. The analysis of magnetic resonance images is also faced with the challenge of small training sets. In this domain, Amit et al.  investiget al. [Another way of dealing with limited training data is to divide rich data—e.g. 3D images—into numerous reduced projections. Shin et al.  comparedet al. [et al. [Roth et al.  compared [et al.  showed tet al. [et al. [Predictions from deep neural networks can be evaluated for use in workflows that also incorporate human experts. In a large dataset of mammography images, Kooi et al.  demonstr [et al.  estimateet al. [et al. [Systems to aid in the analysis of histology slides are also promising use cases for deep learning . Ciresanet al.  develope [et al.  analysed [et al. . In thiset al. [One source of training examples with rich phenotypical annotations is the EHR. Billing information in the form of ICD codes are simple annotations but phenotypic algorithms can combine laboratory tests, medication prescriptions and patient notes to generate more reliable phenotypes. Recently, Lee et al.  developeet al. , but we et al. [weak labels. Such labels are automatically generated and not verified by humans, so they may be noisy or incomplete. In this case, they applied a series of natural language processing (NLP) techniques to the associated chest X-ray radiological reports. They first extracted all diseases mentioned in the reports using a state-of-the-art NLP tool, then applied a new method, NegBio [1 score, which balances precision and recall [text-mined (weakly labelled) pathology categories  or ‘no finding’ otherwise. Further, Wang et al. [Rich clinical information is stored in EHRs. However, manually annotating a large set requires experts and is time-consuming. For chest X-ray studies, a radiologist usually spends a few minutes per example. Generating the number of examples needed for deep learning is infeasibly expensive. Instead, researchers may benefit from using text mining to generate annotations , even ifet al.  proposed, NegBio , to filtd recall ). The red recall  consisteg et al.  used thiAnother example of semi-automated label generation for hand radiograph segmentation employed positive mining, an iterative procedure that combines manual labelling with automatic processing . First, With the exception of natural image-like problems (e.g. melanoma detection), biomedical imaging poses a number of challenges for deep learning. Datasets are typically small, annotations can be sparse, and images are often high-dimensional, multimodal and multi-channel. Techniques like transfer learning, heavy dataset augmentation and the use of multi-view and multi-stream architectures are more common than in the natural image domain. Furthermore, high model sensitivity and specificity can translate directly into clinical value. Thus, prediction evaluation, uncertainty estimation and model interpretation methods are also of great importance in this domain (see Discussion). Finally, there is a need for better pathologist–computer interaction techniques that will allow combining the power of deep learning methods with human expertise and lead to better-informed decisions for patient treatment and care.2.2.Owing to the rapid growth of scholarly publications and EHRs, biomedical text mining has become increasingly important in recent years. The main tasks in biological and clinical text mining include, but are not limited to, named entity recognition (NER), relation/event extraction and information retrieval . Deep leet al. [et al. [et al. [et al. [NER is a task of identifying text spans that refer to a biological concept of a specific class, such as disease or chemical, in a controlled vocabulary or ontology. NER is often needed as a first step in many complex text mining systems. The current state-of-the-art methods typically reformulate the task as a sequence labelling problem and use conditional random fields –77. In ret al.  studied  [et al.  investig [et al.  examined [et al.  exploiteet al. [et al. [et al. [et al. [et al. [Relation extraction involves detecting and classifying semantic relationships between entities from the literature. At present, kernel methods or feature-based approaches are commonly applied –84. Deepet al.  proposed [et al.  employed [et al.  used a C [et al.  experime [et al.  proposedet al. [et al. [et al. [f-score. Among various deep learning approaches, CNNs stand out as the most popular model both in terms of computational complexity and performance, while RNNs have achieved continuous progress.For biotopes event extraction, Li et al.  employed [et al.  used lon [et al.  applied  [et al. ,99. Takeet al. [Information retrieval is a task of finding relevant text that satisfies an information need from within a large document collection. While deep learning has not yet achieved the same level of success in this area as seen in others, the recent surge of interest and work suggest that this may be quickly changing. For example, Mohan et al.  describeet al. .To summarize, deep learning has shown promising results in many biomedical text mining tasks and applications. However, to realize its full potential in this domain, either large amounts of labelled data or technical advancements in current methods coping with limited labelled data are required.2.3.et al. evaluated the extent to which deep learning methods could be applied on top of generic features for domain-specific concept extraction [EHR data include substantial amounts of free text, which remains challenging to approach . Often, traction . They fotraction . This raet al. [In recent work, Yoon et al.  analysedet al. [et al. [Several authors have created reusable feature sets for medical terminologies using NLP and neural embedding models, as popularized by word2vec . Minarroet al.  applied et al. , ICD andet al. ,109 and et al. . Methodset al. , but theet al.  further  [et al.  investiget al. [et al. [et al. [et al. [et al. [et al. [Identifying consistent subgroups of individuals and individual health trajectories from clinical tests is also an active area of research. Approaches inspired by deep learning have been used for both unsupervised feature construction and supervised prediction. Early work by Lasko et al. , combineet al. . In addi [et al.  used a d [et al.  attempte [et al.  built up [et al.  took a d [et al.  used a set al. [Still, recent work has also revealed domains in which deep networks have proven superior to traditional methods. Survival analysis models the time leading to an event of interest from a shared starting point, and in the context of EHR data, often associates these events to subject covariates. Exploring this relationship is difficult, however, given that EHR data types are often heterogeneous, covariates are often missing and conventional approaches require the covariate–event relationship be linear and aligned to a specific starting point . Early aet al.  in turn et al. . The resThere is a computational cost for these methods, however, when compared to traditional, non-neural network approaches. For the exponential family models, despite their scalability , an impo2.4.2.4.1.A dearth of true labels is perhaps among the biggest obstacles for EHR-based analyses that employ machine learning. Popular deep learning (and other machine learning) methods are often used to tackle classification tasks and thus require ground-truth labels for training. For EHRs, this can mean that researchers must hire multiple clinicians to manually read and annotate individual patients' records through a process called chart review. This allows researchers to assign ‘true’ labels, i.e. those that match our best available knowledge. Depending on the application, sometimes the features constructed by algorithms also need to be manually validated and interpreted by clinicians. This can be time-consuming and expensive . Becauseet al. [Successful approaches to date in this domain have sidestepped this challenge by making methodological choices that either reduce the need for labelled examples or use transformations to training data to increase the number of times it can be used before overfitting occurs. For example, the unsupervised and semi-supervised methods that we have discussed reduce the need for labelled examples . The ancet al.  trained et al. . In dataet al. [New New Oil’. In this framing, data are abundant and not a scarce resource. Instead, new approaches to solving problems arise when labelled training data become sufficient to enable them. Based on our review of research on deep learning methods to categorize disease, the latter framing rings true.Numerous commentators have described data as the new oil ,132. Theet al.  describeWe expect improved methods for domains with limited data to play an important role if deep learning is going to transform how we categorize states of human health. We do not expect that deep learning methods will replace expert review. We expect them to complement expert review by allowing more efficient use of the costly practice of manual annotation.2.4.2.To construct the types of very large datasets that deep learning methods thrive on, we need robust sharing of large collections of data. This is, in part, a cultural challenge. We touch on this challenge in the Discussion section. Beyond the cultural hurdles around data sharing, there are also technological and legal hurdles related to sharing individual health records or deep models built from such records. This subsection deals primarily with these challenges.EHRs are designed chiefly for clinical, administrative and financial purposes, such as patient care, insurance and billing . ScienceEven within the same healthcare system, EHRs can be used differently ,137. IndIn the wider picture, standards for EHRs are numerous and evolving. Proprietary systems, indifferent and scattered use of health information standards, and controlled terminologies makes combining and comparison of data across systems challenging . Furtheret al. [Combining or replicating studies across systems thus requires controlling for both the above biases and dealing with mismatching standards. This has the practical effect of reducing cohort size, limiting statistical significance, preventing the detection of weak effects , and reset al.  showed tFinally, even if data were perfectly consistent and compatible across systems, attempts to share and combine EHR data face considerable legal and ethical barriers. Patient privacy can severely restrict the sharing and use of EHR data . Here agSeveral technological solutions have been proposed in this direction, allowing access to sensitive data satisfying privacy and legal concerns. Software like DataShield  and ViPAet al. [et al. [et al. [Even without sharing data, algorithms trained on confidential patient data may present security risks or accidentally allow for the exposure of individual-level patient data. Tramer et al.  showed tet al.  demonstret al. , in this [et al.  develope [et al.  show theet al. [et al. [et al. [et al. and Esteban et al. train models on synthetic data generated under differential privacy and observe performance from a transfer learning evaluation that is only slightly below models trained on the original, real data. Taken together, these results suggest that differentially private GANs may be an attractive way to generate sharable datasets for downstream reanalysis.These attacks also present a potential hazard for approaches that aim to generate data. Choi et al.  propose  [et al.  showed t [et al.  demonstrFederated learning  and secuWhile none of these problems are insurmountable or restricted to deep learning, they present challenges that cannot be ignored. Technical evolution in EHRs and data standards will doubtless ease—although not solve—the problems of data sharing and merging. More problematic are the privacy issues. Those applying deep learning to the domain should consider the potential of inadvertently disclosing the participants' identities. Techniques that enable training on data without sharing the raw data may have a part to play. Training within a differential privacy framework may often be warranted.2.4.3.In April 2016, the European Union adopted new rules regarding the use of personal information, the General Data Protection Regulation . A compoAs datasets become larger and more complex, we may begin to identify relationships in data that are important for human health but difficult to understand. The algorithms described in this review and others like them may become highly accurate and useful for various purposes, including within medical practice. However, to discover and avoid discriminatory applications it will be important to consider interpretability alongside accuracy. A number of properties of genomic and healthcare data will make this difficult.et al.'s retraction [First, research samples are frequently non-representative of the general population of interest; they tend to be disproportionately sick , male 1 and Eurotraction ). In somtraction ). When wtraction  or lead traction .There is a small but growing literature on the prevention and mitigation of data leakage , as well2.4.4.et al. [The longitudinal analysis follows a population across time, for example, prospectively from birth or from the onset of particular conditions. In large patient populations, longitudinal analyses such as the Framingham Heart Study  and the et al.  used autet al.  and the et al. . This ma3.The study of cellular structure and core biological processes—transcription, translation, signalling, metabolism, etc.—in humans and model organisms will greatly impact our understanding of human disease over the long horizon . PredictProgress has been rapid in genomics and imaging, fields where important tasks are readily adapted to well-established deep learning paradigms. One-dimensional CNNs and RNNs are well suited for tasks related to DNA- and RNA-binding proteins, epigenomics and RNA splicing. Two-dimensional CNNs are ideal for segmentation, feature extraction and classification in fluorescence microscopy images . Other a3.1.Gene expression technologies characterize the abundance of many thousands of RNA transcripts within a given organism, tissue or cell. This characterization can represent the underlying state of the given system and can be used to study heterogeneity across samples as well as how the system reacts to perturbation. While gene expression measurements were traditionally made by quantitative polymerase chain reaction, low-throughput fluorescence-based methods and microarray technologies, the field has shifted in recent years to primarily performing RNA sequencing (RNA-seq) to catalogue whole transcriptomes. As RNA-seq continues to fall in price and rise in throughput, sample sizes will increase and training deep models to study gene expression will become even more useful.Pseudomonas aeruginosa experiments [Already several deep learning approaches have been applied to gene expression data with varying aims. For instance, many researchers have applied unsupervised deep learning models to extract meaningful representations of gene modules or sample clusters. Denoising autoencoders have been used to cluster yeast expression microarrays into known modules representing cell cycle processes  and to seriments ,182 and eriments . These uDeep learning approaches are also being applied to gene expression prediction tasks. For example, a deep neural network with three hidden layers outperformed linear regression in inferring the expression of over 20 000 target genes based on a representative, well-connected set of about 1000 landmark genes . Howeveret al. [Epigenomic data, combined with deep learning, may have sufficient explanatory power to infer gene expression. For instance, the DeepChrome CNN  improvedet al.  combinedDeep learning applied to gene expression data is still in its infancy, but the future is bright. Many previously untestable hypotheses can now be interrogated as deep learning enables analysis of increasing amounts of data generated by new technologies. For example, the effects of cellular heterogeneity on basic biology and disease aetiology can now be explored by single-cell RNA-seq and high-throughput fluorescence-based imaging, techniques we discuss below that will benefit immensely from deep learning approaches.3.2.LMNA) gene can lead to specific variants of dilated cardiomyopathy and limb-girdle muscular dystrophy [Pre-mRNA transcripts can be spliced into different isoforms by retaining or skipping subsets of exons or including parts of introns, creating enormous spatio-temporal flexibility to generate multiple distinct proteins from a single gene. This remarkable complexity can lend itself to defects that underlie many diseases. For instance, splicing mutations in the lamin A  [in vivo binding maps of TFs [TFs are proteins that bind regulatory DNA in a sequence-specific manner to modulate the activation and repression of gene transcription. High-throughput leotides  provide  around) . TFs cans of TFs . Large rs of TFs . Owing tin vitro and in vivo TF binding datasets that associate collections of synthetic DNA sequences or genomic DNA sequences to binary labels (bound/unbound) or continuous measures of binding. The most common class of TF binding models in the literature are those that only model the DNA sequence affinity of TFs from in vitro and in vivo binding data. The earliest models were based on deriving simple, compact, interpretable sequence motif representations such as position weight matrices (PWMs) and other biophysically inspired models [Several machine learning approaches have been developed to learn generative and discriminative models of TF binding from d models –200. Thed models ,202.et al. [in vitro and in vivo assays against random DNA sequences matched for dinucleotide sequence composition. The convolutional layers learn pattern detectors reminiscent of PWMs from a one-hot encoding of the raw input DNA sequences. DeepBind outperformed several state-of-the-art methods from the DREAM5 in vitro TF-DNA motif recognition challenge [In 2015, Alipanahi et al.  developehallenge . Althoughallenge  and accuhallenge –208. Spehallenge ,210.in vivo multiple TFs compete or cooperate to occupy DNA binding sites, resulting in complex combinatorial co-binding landscapes. To take advantage of this shared structure in in vivo TF binding data, multi-task neural network architectures have been developed that explicitly share parameters across models for multiple TFs [While most of these methods learn independent models for different TFs, iple TFs ,211,212.in vivo TF binding landscapes in new cell types not used during training. One approach for generalizing TF binding predictions to new cell types is to learn models that integrate DNA sequence inputs with other cell-type-specific data modalities that modulate in vivo TF binding such as surrogate measures of TF concentration (e.g. TF gene expression) and chromatin state. Arvey et al. [in vivo TF binding prediction within and across cell types. Several ‘footprinting’-based methods have also been developed that learn to discriminate bound from unbound instances of known canonical motifs of a target TF based on high-resolution footprint patterns of chromatin accessibility that are specific to the target TF [The above-mentioned TF binding prediction models that use only DNA sequences as inputs have a fundamental limitation. Because the DNA sequence of a genome is the same across different cell types and states, a sequence-only model of TF binding cannot predict different y et al.  showed target TF . Howeverin vivo TF Binding Site Prediction Challenge’ was introduced to systematically evaluate the genome-wide performance of methods that can predict TF binding across cell states by integrating DNA sequence and in vitro DNA shape with cell-type-specific chromatin accessibility and gene expression [Recently, a community challenge known as the ‘ENCODE-DREAM pression . A deep pression . FactorNpression . This toet al. [Singh et al.  developeet al. ,220. Thein vivo TF binding prediction methods leverage the strong correlation between combinatorial binding landscapes of multiple TFs. Given a partially complete panel of binding profiles of multiple TFs in multiple cell types, a deep learning method called TFImpute learns to predict the missing binding profile of a target TF in some target cell type in the panel based on the binding profiles of other TFs in the target cell type and the binding profile of the target TF in other cell types in the panel [Another class of imputation-based cross cell type he panel . HoweverIt is worth noting that TF binding prediction methods in the literature based on neural networks and other machine learning approaches choose to sample the set of bound and unbound sequences in a variety of different ways. These choices and the choice of performance evaluation measures significantly confound systematic comparison of model performance (see Discussion).et al. [in silico mutation maps for identifying important predictive nucleotides in input DNA sequences by exhaustively forward propagating perturbations to individual nucleotides to record the corresponding change in output prediction. Shrikumar et al. [et al. [Several methods have also been developed to interpret neural network models of TF binding. Alipanahi et al.  visualizr et al.  proposed [et al.  develope3.4.3.4.1.Multiple TFs act in concert to coordinate changes in gene regulation at the genomic regions known as promoters and enhancers. Each gene has an upstream promoter, essential for initiating that gene's transcription. The gene may also interact with multiple enhancers, which can amplify transcription in particular cellular contexts. These contexts include different cell types in development or environmental stresses.Promoters and enhancers provide a nexus where clusters of TFs and binding sites mediate downstream gene regulation, starting with transcription. The gold standard to identify an active promoter or enhancer requires demonstrating its ability to affect transcription or other downstream gene products. Even extensive biochemical TF binding data has thus far proven insufficient on its own to accurately and comprehensively locate promoters and enhancers. We lack sufficient understanding of these elements to derive a mechanistic ‘promoter code’ or ‘enhancer code’. But extensive labelled data on promoters and enhancers lends itself to probabilistic classification. The complex interplay of TFs and chromatin leading to the emergent properties of promoter and enhancer activity seems particularly apt for representation by deep neural networks.3.4.2.Despite decades of work, computational identification of promoters remains a stubborn problem . Researc3.4.3.Recognizing enhancers presents additional challenges. Enhancers may be up to 1 000 000 bp away from the affected promoter, and even within introns of other genes . EnhanceSeveral neural network approaches yielded promising results in enhancer prediction. Both Basset  and DeepComparing the performance of enhancer prediction methods illustrates the problems in using metrics created with different benchmarking procedures. Both the Basset and DeepEnhancer studies include comparisons to a baseline SVM approach, gkm-SVM . The Bas3.4.4.In addition to the location of enhancers, identifying enhancer–promoter interactions in three-dimensional space will provide critical knowledge for understanding transcriptional regulation. SPEID used a CNN to predict these interactions with only sequence and the location of putative enhancers and promoters along a one-dimensional chromosome . It comp3.5.Prediction of miRNAs and miRNA targets is of great interest, as they are critical components of gene regulatory networks and are often conserved across great evolutionary distance ,234. WhiAs in other applications, deep learning promises to achieve equal or better performance in predictive tasks by automatically engineering complex features to minimize an objective function. Two recently published tools use different recurrent neural network-based architectures to perform miRNA and target prediction with solely sequence data as input ,237. Tho3.6.Proteins play fundamental roles in almost all biological processes, and understanding their structure is critical for basic biology and drug development. UniProt currently has about 94 million protein sequences, yet fewer than 100 000 proteins across all species have experimentally solved structures in Protein Data Bank (PDB). As a result, computational structure prediction is essential for a majority of proteins. However, this is very challenging, especially when similar solved structures, called templates, are not available in PDB. Over the past several decades, many computational methods have been developed to predict aspects of protein structure such as secondary structure, torsion angles, solvent accessibility, inter-residue contact maps, disorder regions and side-chain packing. In recent years, multiple deep learning architectures have been applied, including DBNs, LSTMs, CNNs and deep convolutional neural fields ,238.Here, we focus on deep learning methods for two representative sub-problems: secondary structure prediction and contact map prediction. Secondary structure refers to local conformation of a sequence segment, while a contact map contains information on all residue–residue contacts. Secondary structure prediction is a basic problem and an almost essential module of any protein structure prediction package. Contact prediction is much more challenging than secondary structure prediction, but it has a much larger impact on tertiary structure prediction. In recent years, the accuracy of contact prediction has greatly improved ,239–241.et al. developed a DeepCNF model that improved Q3 and Q8 accuracy as well as prediction of solvent accessibility and disorder regions [One can represent protein secondary structure with three different states  or eight finer-grained states. The accuracy of a three-state prediction is called Q3, and accuracy of an eight-state prediction is called Q8. Several groups ,242,243  regions ,238. Deeab initio folding of proteins without good templates in PDB. Coevolution analysis is effective for proteins with a very large number (more than 1000) of sequence homologues [Protein contact prediction and contact-assisted folding (i.e. folding proteins using predicted contacts as restraints) represent a promising new direction for mologues , but farmologues  and Coinmologues  have shomologues , DNCON [mologues  and PConmologues . Howevermologues .et al. [1 score on free-modelling targets as well as the whole set of targets. In CAMEO (which can be interpreted as a fully automated CASP) [Recently, Wang et al.  proposedet al. ), Raptored CASP) , its preed CASP) . RaptorXab initio folding is becoming much easier with the advent of direct evolutionary coupling analysis and deep learning techniques. We expect further improvements in contact prediction for proteins with fewer than 1000 homologues by studying new deep network architectures. The deep learning methods summarized above also apply to interfacial contact prediction for protein complexes but may be less effective because on average protein complexes have fewer sequence homologues. Beyond secondary structure and contact maps, we anticipate increased attention to predicting 3D protein structure directly from amino acid sequence and single residue evolutionary information [Taken together, ormation .3.7.Complementing computational prediction approaches, cryo-electron microscopy (cryo-EM) allows near-atomic resolution determination of protein models by comparing individual electron micrographs . DetaileSome components of cryo-EM image processing remain difficult to automate. For instance, in particle picking, micrographs are scanned to identify individual molecular images that will be used in structure refinement. In typical applications, hundreds of thousands of particles are necessary to determine a structure to near-atomic resolution, making manual selection impractical . TypicalDownstream of particle picking, deep learning is being applied to other aspects of cryo-EM image processing. Statistical manifold learning has been implemented in the software package ROME to classify selected particles and elucidate the different conformations of the subject molecule necessary for accurate 3D structures . These r3.8.Protein–protein interactions (PPIs) are highly specific and non-accidental physical contacts between proteins, which occur for purposes other than generic protein production or degradation . AbundanMany machine learning approaches to PPI have focused on text mining the literature ,263, butet al. [et al. [One of the key difficulties in applying deep learning techniques to binding prediction is the task of representing peptide and protein sequences in a meaningful way. DeepPPI  made PPIet al.  applied  [et al.  used deeet al. [Beyond predicting whether or not two proteins interact, Du et al.  employedBecause many studies used predefined higher-level features, one of the benefits of deep learning—automatic feature extraction—is not fully leveraged. More work is needed to determine the best ways to represent raw protein sequence information so that the full benefits of deep learning as an automatic feature extractor can be realized.3.9.An important type of PPI involves the immune system's ability to recognize the body's own cells. The major histocompatibility complex (MHC) plays a key role in regulating this process by binding antigens and displaying them on the cell surface to be recognized by T cells. Owing to its importance in immunity and immune response, peptide–MHC binding prediction is a useful problem in computational biology, and one that must account for the allelic diversity in MHC-encoding gene region.et al. [Shallow, feed-forward neural networks are competitive methods and have made progress towards pan-allele and pan-length peptide representations. Sequence alignment techniques are useful for representing variable-length peptides as uniform-length features ,269. Foret al.  developeet al. [et al. [et al. found that the top methods—NetMHC, NetMHCpan, MHCflurry and MHCnuggets—showed comparable performance, but large differences in speed. Convolutional neural networks  showed comparatively poor performance, while shallow networks and RNNs performed the best. They found that MHCnuggets—the recurrent neural network—was by far the fastest-training among the top performing methods.Deep learning's unique flexibility was recently leveraged by Bhattacharya et al. , who use [et al.  develope3.10.k-core size and graph density, this work showed that deep learning can effectively reduce graph dimensionality while retaining much of its structural information.Because interacting proteins are more likely to share a similar function, the connectivity of a PPI network itself can be a valuable information source for the prediction of protein function . To incoet al. [An important challenge in PPI network prediction is the task of combining different networks and types of networks. Gligorijevic et al.  developeet al. [Hamilton et al.  addresseet al.  optimize3.11.A field poised for dramatic revolution by deep learning is bioimage analysis. Thus far, the primary use of deep learning for biological images has been for segmentation—that is, for the identification of biologically relevant structures in images such as nuclei, infected cells or vasculature—in fluorescence or even brightfield channels . Once thet al. [We anticipate an additional paradigm shift in bioimaging that will be brought about by deep learning: what if images of biological samples, from simple cell cultures to three-dimensional organoids and tissue samples, could be mined for much more extensive biologically meaningful information than is currently standard? For example, a recent study demonstrated the ability to predict lineage fate in haematopoietic cells up to three generations in advance of differentiation . In biomet al.  demonstrThe impact of further improvements on biomedicine could be enormous. Comparing cell population morphologies using conventional methods of segmentation and feature extraction has already proven useful for functionally annotating genes and alleles, identifying the cellular target of small molecules, and identifying disease-specific phenotypes suitable for drug screening –290. Dee3.12.in situ hybridization or the heterogeneity of epigenomic patterns with single-cell Hi-C or ATAC-seq [Single-cell methods are generating excitement as biologists characterize the vast heterogeneity within unicellular species and between cells of the same tissue type in the same organism . For insATAC-seq ,294. JoiATAC-seq .et al. [However, large challenges exist in studying single cells. Relatively few cells can be assayed at once using current droplet, imaging or microwell technologies, and low-abundance molecules or modifications may not be detected by chance due to a phenomenon known as dropout, not to be confused with the dropout layer of deep learning. To solve this problem, Angermueller et al.  trained et al. ,297. Deeet al. .et al. [Examining populations of single cells can reveal biologically meaningful subsets of cells as well as their underlying gene regulatory networks . Unfortuet al.  classifiet al. [Neural networks can also learn low-dimensional representations of single-cell gene expression data for visualization, clustering and other tasks. Both scvis  and scVIet al.  developeet al. , they deet al.  and relaThe sheer quantity of omic information that can be obtained from each cell, as well as the number of cells in each dataset, uniquely position single-cell data to benefit from deep learning. In the future, lineage tracing could be revolutionized by using autoencoders to reduce the feature space of transcriptomic or variant data followed by algorithms to learn optimal cell differentiation trajectories  or by fe3.13.Metagenomics, which refers to the study of genetic material—16S rRNA or whole-genome shotgun DNA—from microbial communities, has revolutionized the study of micro-scale ecosystems within and around us. In recent years, machine learning has proved to be a powerful tool for metagenomic analysis. 16S rRNA has long been used to deconvolve mixtures of microbial genomes, yet this ignores more than 99% of the genomic content. Subsequent tools aimed to classify 300–3000 bp reads from complex mixtures of microbial genomes based on tetranucleotide frequencies, which differ across organisms , using sMost neural networks are used for phylogenetic classification or functional annotation from sequence data where there is ample data for training. Neural networks have been applied successfully to gene annotation  was able to classify wound severity from microbial species present in the wound . Recentlet al.  associatet al. .Challenges remain in applying deep neural networks to metagenomics problems. They are not yet ideal for phenotype classification because most studies contain tens of samples and hundreds or thousands of features (species). Such underdetermined, or ill-conditioned, problems are still a challenge for deep neural networks that require many training examples. Also, due to convergence issues , taxonomHowever, because RNNs have been applied to base calls for the Oxford Nanopore long-read sequencer with some success   with high specificity and sensitivity and improving the accuracy of new types of data such as nanopore sequencing. These two tasks are critical for studying rare variation, allele-specific transcription and translation, and splice site mutations. In the clinical realm, sequencing of rare tumour clones and other genetic diseases will require the accurate calling of SNPs and indels.Current methods achieve relatively high (greater than 99%) precision at 90% recall for SNPs and indel calls from Illumina short-read data , yet thiVariant calling will benefit more from optimizing neural network architectures than from developing features by hand. An interesting and informative next step would be to rigorously test if encoding raw sequence and quality data as an image, tensor or some other mixed format produces the best variant calls. Because many of the latest neural network architectures  are already optimized for and pre-trained on generic, large-scale image datasets , encodinet al. [E. coli sequence from MinION nanopore electric current data with higher per-base accuracy than the proprietary hidden Markov model-based algorithm Metrichor. Unfortunately, training any neural network requires a large amount of data, which is often not available for new sequencing technologies. To circumvent this, one very preliminary study simulated mutations and spiked them into somatic and germline RNA-seq data, then trained and tested a neural network on simulated paired RNA-seq and exome sequencing data [In limited experiments, DeepVariant was robust to sequencing depth, read length and even species . Howeveret al.  used biding data . HoweverMethod development for interpreting new types of sequencing data has historically taken two steps: first, easily implemented hard cutoffs that prioritize specificity over sensitivity, then expert development of probabilistic models with hand-developed inputs . We anti3.15.Artificial neural networks were originally conceived as a model for computation in the brain . AlthougCNNs were originally conceived as faithful models of visual information processing in the primate visual system, and are still considered so . The actEven when they are not directly modelling biological neurons, deep networks have been a useful computational tool in neuroscience. They have been developed as statistical time-series models of neural activity in the brain. And in contrast to the encoding models described earlier, these models are used for decoding neural activity, for instance, in brain–machine interfaces . They haIt is an exciting time for neuroscience. Recent rapid progress in deep networks continues to inspire new machine learning-based models of brain computation . And neu4.Given the need to make better, faster interventions at the point of care—incorporating the complex calculus of a patient's symptoms, diagnostics and life history—there have been many attempts to apply deep learning to patient treatment. Success in this area could help to enable personalized healthcare or precision medicine ,354. Ear4.1.In 1996, Tu  comparedWhile further progress has been made in using deep learning for clinical decision-making, it is hindered by a challenge common to many deep learning applications: it is much easier to predict an outcome than to suggest an action to change the outcome. Several attempts ,123 at ret al. [et al. [A critical challenge in providing treatment recommendations is identifying a causal relationship for each recommendation. Causal inference is often framed in terms of the counterfactual question . Johansset al.  use deep [et al.  first crA common challenge for deep learning is the interpretability of the models and their predictions. The task of clinical decision-making is necessarily risk-averse, so model interpretability is key. Without clear reasoning, it is difficult to establish trust in a model. As described above, there has been some work to directly assign treatment plans without interpretability; however, the removal of human experts from the decision-making loop make the models difficult to integrate with clinical practice. To alleviate this challenge, several studies have attempted to create more interpretable deep models, either specifically for healthcare or as a general procedure for deep learning (see Discussion).4.1.1.et al. [A common application for deep learning in this domain is the temporal structure of healthcare records. Many studies –365 haveet al.  used deeet al.  have relet al. , but a g4.1.2.et al. [et al. [A clinical deep learning task that has been more successful is the assignment of patients to clinical trials. Ithapu et al.  used a r [et al.  applied 4.2.Drug repositioning (or repurposing) is an attractive option for delivering new drugs to the market because of the high costs and failure rates associated with more traditional drug discovery approaches ,371. A det al. [et al. [For example, Menden et al.  used a s [et al.  used gen [et al.  to trainet al. [et al. [et al. [Drug repositioning can also be approached by attempting to predict novel drug–target interactions and then repurposing the drug for the associated indication ,382. Wanet al.  devised  [et al.  extendedDrug repositioning appears an obvious candidate for deep learning both because of the large amount of high-dimensional data available and the complexity of the question being asked. However, perhaps the most promising piece of work in this space  is more 4.3.4.3.1.High-throughput chemical screening in biomedical research aims to improve therapeutic options over a long-term horizon . The objComputational work in this domain aims to identify sufficient candidate active compounds without exhaustively screening libraries of hundreds of thousands or millions of chemicals. Predicting chemical activity computationally is known as virtual screening. An ideal algorithm will rank a sufficient number of active compounds before the inactives, but the rankings of actives relative to other actives and inactives are less important . Computaet al. [et al. [Ligand-based approaches train on chemicals' features without modelling target features (e.g. protein structure). Neural networks have a long history in this domain ,23, and et al. ) exploreet al. ,394, wit [et al. , a deep The nuanced Tox21 performance may be more reflective of the practical challenges encountered in ligand-based chemical screening than the extreme enthusiasm generated by the Merck competition. A study of 22 ADMET tasks demonstrated that there are limitations to multi-task transfer learning that are in part a consequence of the degree to which tasks are related . Some of4.3.2.Much of the recent excitement in this domain has come from what could be considered a creative experimentation phase, in which deep learning has offered novel possibilities for feature representation and modelling of chemical compounds. A molecular graph, where atoms are labelled nodes and bonds are labelled edges, is a natural way to represent a chemical structure. Chemical features can be represented as a list of molecular descriptors such as molecular weight, atom counts, functional groups, charge representations, summaries of atom–atom relationships in the molecular graph, and more sophisticated derived properties . Traditiet al. [et al. [Virtual screening and chemical property prediction have emerged as one of the major applications areas for graph-based neural networks. Duvenaud et al.  generali [et al.  applied  [et al. –407. Mor [et al. ,409 addret al. [one task-specific active compound and one inactive compound, the model is able to generalize reasonably well because it has learned an informative embedding function from the related tasks. Random forests, which cannot take advantage of the related training tasks, trained in the same setting are only slightly better than a random classifier. Despite the success on Tox21, performance on MUV datasets, which contains assays designed to be challenging for chemical informatics algorithms, is considerably worse. The authors also demonstrate the limitations of transfer learning as embeddings learned from the Tox21 assays have little utility for a drug adverse reaction dataset.Advances in chemical representation learning have also enabled new strategies for learning chemical–chemical similarity functions. Altae-Tran et al.  developeThese novel learned chemical feature representations may prove to be essential for accurately predicting why some compounds with similar structures yield similar target effects and others produce drastically different results. Currently, these methods are enticing but do not necessarily outperform classic approaches by a large margin. The neural fingerprints  were narWe remain optimistic about the potential of deep learning and specifically representation learning in drug discovery. Rigorous benchmarking on broad and diverse prediction tasks will be as important as novel neural network architectures to advance the state of the art and convincingly demonstrate superiority over traditional cheminformatics techniques. Fortunately, there has recently been much progress in this direction. The DeepChem software ,412 and One open question in ligand-based screening pertains to the benefits and limitations of transfer learning. Multi-task neural networks have shown the advantages of jointly modelling many targets ,394. Oth4.3.3.When protein structure is available, virtual screening has traditionally relied on docking programs to predict how a compound best fits in the target's binding site and score the predicted ligand–target complex . Recentlet al. [Structure-based deep learning methods differ in whether they use experimentally derived or predicted ligand–target complexes and how they represent the 3D structure. The Atomic CNN  and Topoet al.  use a doet al. .et al. [There are two established options for representing a protein–compound complex. One option, a 3D grid, can featurize the input complex ,419. Eacet al.  demonstr4.3.4.60 synthesizable organic molecules with drug-like properties without explicit enumeration [De novo drug design attempts to model the typical design–synthesize–test cycle of drug discovery ,421. It meration . To testmeration .et al. [As deep learning models that directly output (molecular) graphs remain under-explored, generative neural networks for drug design typically represent chemicals with the simplified molecular-input line-entry system (SMILES), a standard string-based representation with characters that represent atoms, bonds and rings . This alet al.  designedet al. . A drawbet al. .Another approach to de novo design is to train character-based RNNs on large collections of molecules, for example, ChEMBL , to firs5.Despite the disparate types of data and scientific goals in the learning tasks covered above, several challenges are broadly important for deep learning in the biomedical domain. Here, we examine these factors that may impede further progress, ask what steps have already been taken to overcome them, and suggest future research directions.5.1.Some of the challenges in applying deep learning are shared with other machine learning methods. In particular, many problem-specific optimizations described in this review reflect a recurring universal trade-off—controlling the flexibility of a model in order to maximize predictivity. Methods for adjusting the flexibility of deep learning models include dropout, reduced data projections and transfer learning (described below). One way of understanding such model optimizations is that they incorporate external information to limit model flexibility and thereby improve predictions. This balance is formally described as a trade-off between ‘bias and variance’ .Although the bias-variance trade-off is common to all machine learning applications, recent empirical and theoretical observations suggest that deep learning models may have uniquely advantageous generalization properties ,429. Nev5.1.1.Making predictions in the presence of high-class imbalance and differences between training and generalization data are a common feature of many large biomedical datasets, including deep learning models of genomic features, patient classification, disease detection and virtual screening. Prediction of TF binding sites exemplifies the difficulties with learning from highly imbalanced data. The human genome has three billion base pairs, and only a small fraction of them are implicated in specific biochemical activities. Less than 1% of the genome can be confidently labelled as bound for most TFs.Estimating the false discovery rate (FDR) is a standard method of evaluation in genomics that can also be applied to deep learning model predictions of genomic features. Using deep learning predictions for targeted validation experiments of specific biochemical activities necessitates a more stringent FDR . However, when predicted biochemical activities are used as features in other models, such as gene expression models, a low FDR may not be necessary.What is the correspondence between FDR metrics and commonly used classification metrics such as AUPR and AUROC? AUPR evaluates the average precision, or equivalently, the average FDR across all recall thresholds. This metric provides an overall estimate of performance across all possible use cases, which can be misleading for targeted validation experiments. For example, classification of TF binding sites can exhibit a recall of 0% at 10% FDR and AUPR greater than 0.6. In this case, the AUPR may be competitive, but the predictions are ill-suited for targeted validation that can only examine a few of the highest-confidence predictions. Likewise, AUROC evaluates the average recall across all false positive rate (FPR) thresholds, which is often a highly misleading metric in class-imbalanced domains ,430. Con5.1.2.Genome-wide continuous signals are commonly formulated into classification labels through signal peak detection. ChIP-seq peaks are used to identify locations of TF binding and histone modifications. Such procedures rely on thresholding criteria to define what constitutes a peak in the signal. This inevitably results in a set of signal peaks that are close to the threshold, not sufficient to constitute a positive label but too similar to positively labelled examples to constitute a negative label. To avoid an arbitrary label for these examples, they may be labelled as ‘ambiguous’. Ambiguously labelled examples can then be ignored during model training and evaluation of recall and FDR. The correlation between model predictions on these examples and their signal values can be used to evaluate if the model correctly ranks these examples between positive and negative examples.5.1.3.In assessing the upper bound on the predictive performance of a deep learning model, it is necessary to incorporate inherent between-study variation inherent to biomedical research . Study-l5.2.Deep learning-based solutions for biomedical applications could substantially benefit from guarantees on the reliability of predictions and a quantification of uncertainty. Owing to biological variability and precision limits of equipment, biomedical data do not consist of precise measurements but of estimates with noise. Hence, it is crucial to obtain uncertainty measures that capture how noise in input values propagates through deep neural networks. Such measures can be used for reliability assessment of automated decisions in clinical and public health applications, and for guarding against model vulnerabilities in the face of rare or adversarial cases . Moreoveet al. [et al. [et al. [In classification tasks, confidence calibration is the problem of using classifier scores to predict class membership probabilities that match the true membership likelihoods. These membership probabilities can be used to assess the uncertainty associated with assigning the example to each of the classes. Guo et al.  observedet al. . In addi [et al.  that des [et al.  discover [et al.  used temAn alternative approach for obtaining principled uncertainty estimates from deep learning models is to use Bayesian neural networks. Deep learning models are usually trained to obtain the most likely parameters given the data. However, choosing the single most likely set of parameters ignores the uncertainty about which set of parameters (among the possible models that explain the given dataset) should be used. This sometimes leads to uncertainty in predictions when the chosen likely parameters produce high-confidence but incorrect results. On the other hand, the parameters of Bayesian neural networks are modelled as full probability distributions. This Bayesian approach comes with a whole host of benefits, including better calibrated confidence estimates  and moreet al. [et al. [Several other techniques have been proposed for effectively estimating predictive uncertainty as uncertainty quantification for neural networks continues to be an active research area. Recently, McClure & Kriegeskorte  observedet al.  introduc [et al.  proposed [et al. .Despite the success and popularity of deep learning, some deep learning models can be surprisingly brittle. Researchers are actively working on modifications to deep learning frameworks to enable them to handle probability and embrace uncertainty. Most notably, Bayesian modelling and deep learning are being integrated with renewed enthusiasm. As a result, several opportunities for innovation arise: understanding the causes of model uncertainty can lead to novel optimization and regularization techniques, assessing the utility of uncertainty estimation techniques on various model architectures and structures can be very useful to practitioners, and extending Bayesian deep learning to unsupervised settings can be a significant breakthrough . Unfortu5.3.As deep learning models achieve state-of-the-art performance in a variety of domains, there is a growing need to make the models more interpretable. Interpretability matters for two main reasons. First, a model that achieves breakthrough performance may have identified patterns in the data that practitioners in the field would like to understand. However, this would not be possible if the model is a black box. Second, interpretability is important for trust. If a model is making medical diagnoses, it is important to ensure the model is making decisions for reliable reasons and is not focusing on an artefact of the data. A motivating example of this can be found in Ba & Caruana , where aAs the concept of interpretability is quite broad, many methods described as improving the interpretability of deep learning models take disparate and often complementary approaches.5.3.1.Several approaches ascribe importance on an example-specific basis to the parts of the input that are responsible for a particular output. These can be broadly divided into perturbation- and backpropagation-based approaches.et al. [et al. [et al. [et al. [Perturbation-based approaches change parts of the input and observe the impact on the output of the network. Alipanahi et al.  and Zhouet al.  scored g [et al.  used a s [et al.  inserted [et al.  introduc [et al.  applied  [et al. .et al. [A common drawback to perturbation-based approaches is computational efficiency: each perturbed version of an input requires a separate forward propagation through the network to compute the output. As noted by Shrikumar et al. , such meet al.  solve anet al. [Backpropagation-based methods, in which the signal from a target output neuron is propagated backwards to the input layer, are another way to interpret deep networks that sidestep inefficiencies of the perturbation-based methods. A classic example of this is calculating the gradients of the output with respect to the input  to compuet al.  proposedet al. ,456. Netet al. ,457. Bacet al. , and newet al. ,459,460.et al.  noted thet al. , which h5.3.2.Another approach to understanding the network's predictions is to find artificial inputs that produce similar hidden representations to a chosen example. This can elucidate the features that the network uses for prediction and drop the features that the network is insensitive to. In the context of natural images, Mahendran & Vedaldi  introducet al. [A related idea is ‘caricaturization’, where an initial image is altered to exaggerate patterns that the network searches for . This iset al.  leverage5.3.3.et al. [et al. [Activation maximization can reveal patterns detected by an individual neuron in the network by generating images which maximally activate that neuron, subject to some regularizing constraints. This technique was first introduced in Ehran et al.  and applet al. ,466,468. [et al.  applied 5.3.4.et al. [et al. [et al. [et al. [Several interpretation methods are specifically tailored to recurrent neural network architectures. The most common form of interpretability provided by RNNs is through attention mechanisms, which have been used in diverse problems such as image captioning and machine translation to select portions of the input to focus on generating a particular output ,470. Demet al.  applied  [et al.  used a h [et al.  leverage [et al.  later exet al. [Visualizing the activation patterns of the hidden state of a recurrent neural network can also be instructive. Early work by Ghosh & Karamcheti  used cluet al.  showed tet al.  allows iet al. [et al. [Another strategy, adopted by Lanchatin et al.  looks at [et al. , illustrMurdoch & Szlam  showed t5.3.5.Interpretation of embedded or latent space features learned through generative unsupervised models can reveal underlying patterns otherwise masked in the original input. Embedded feature interpretation has been emphasized mostly in image- and text-based applications ,478, butFor example, Way & Greene trained a VAE on gene expression from The Cancer Genome Atlas (TCGA)  and use et al. [et al. [Other approaches have used interpolation through latent space embeddings learned by GANs to interpret unobserved intermediate states. For example, Osokin et al.  trained  [et al.  trained 5.3.6.et al. [It can often be informative to understand how the training data affects model learning. Towards this end, Koh & Liang  used infet al.  used graet al. [Finally, it is sometimes possible to train the model to provide justifications for its predictions. Lei et al.  used a g5.3.7.While deep learning lags behind most Bayesian models in terms of interpretability, the interpretability of deep learning is comparable to or exceeds that of many other widely used machine learning methods such as random forests or SVMs. While it is possible to obtain importance scores for different inputs in a random forest, the same is true for deep learning. Similarly, SVMs trained with a nonlinear kernel are not easily interpretable because the use of the kernel means that one does not obtain an explicit weight matrix. Finally, it is worth noting that some simple machine learning methods are less interpretable in practice than one might expect. A linear model trained on heavily engineered features might be difficult to interpret as the input features themselves are difficult to interpret. Similarly, a decision tree with many nodes and branches may also be difficult for a human to make sense of.There are several directions that might benefit the development of interpretability techniques. The first is the introduction of gold standard benchmarks that different interpretability approaches could be compared against, similar in spirit to how the ImageNet  and CIFA5.4.et al. [A lack of large-scale, high-quality, correctly labelled training data have impacted deep learning in nearly all applications we have discussed. The challenges of training complex, high-parameter neural networks from few examples are obvious, but uncertainty in the labels of those examples can be just as problematic. In genomics, labelled data may be derived from an experimental assay with known and unknown technical artefacts, biases and error profiles. It is possible to weight training examples or construct Bayesian models to account for uncertainty or non-independence in the data, as described in the TF binding example above. As another example, Park et al.  estimateFor some types of data, especially images, it is straightforward to augment training datasets by splitting a single labelled example into multiple examples. For example, an image can easily be rotated, flipped or translated and retain its label . 3D MRI Simulated or semi-synthetic training data have been employed in multiple biomedical domains, though many of these ideas are not specific to deep learning. Training and evaluating on simulated data, for instance, generating synthetic TF binding sites with PWMs  or RNA-sData can be simulated to create negative examples when only positive training instances are available. DANN  adopts tIn settings where the experimental observations are biased towards positive instances, such as MHC protein and peptide ligand binding affinity , or the Multimodal, multi-task and transfer learning, discussed in detail below, can also combat data limitations to some degree. There are also emerging network architectures, such as Diet Networks for high-dimensional SNP data . These u5.5.Efficiently scaling deep learning is challenging, and there is a high computational cost  associated with training neural networks and using them to make predictions. This is one of the reasons why neural networks have only recently found widespread use .et al. [Many have sought to curb these costs, with methods ranging from the very applied  to et al.  inferredet al.  or the tet al. . Some haet al. .While steady improvements in GPU hardware may alleviate this issue, it is unclear whether advances will occur quickly enough to keep pace with the growing biological datasets and increasingly complex neural networks. Much has been done to minimize the memory requirements of neural networks ,509,510,Distributed computing is a general solution to intense computational requirements and has enabled many large-scale deep learning efforts. Some types of distributed computation ,514 are Cloud computing, which has already seen wide adoption in genomics , could f5.6.A robust culture of data, code and model sharing would speed advances in this domain. The cultural barriers to data sharing, in particular, are perhaps best captured by the use of the term ‘research parasite’ to describe scientists who use data from other researchers . A fieldet al. [The sharing of high-quality, labelled datasets will be especially valuable. In addition, researchers who invest time to preprocess datasets to be suitable for deep learning can make the preprocessing code  when jointly learned with other modalities (audio or text) . Deep gret al. [et al. [et al. [k-means.Jha et al.  showed t [et al.  trained  [et al.  used DBN [et al. . This apMultimodal learning with CNNs is usually implemented as a collection of individual networks in which each learns representations from the single data type. These individual representations are further concatenated before or within fully connected layers. FIDDLE  is an exet al. [et al. [Multi-task learning is an approach related to transfer learning. In a multi-task learning framework, a model learns a number of tasks simultaneously such that features are shared across them. DeepSEA  implemenet al.  demonstret al. ,394 and et al. ,543. Kea [et al.  systematet al. [Multi-task learning is complementary to multimodal and transfer learning. All three techniques can be used together in the same model. For example, Zhang et al.  combinedDespite demonstrated improvements, transfer learning approaches pose challenges. There are no theoretically sound principles for pre-training and fine-tuning. Best practice recommendations are heuristic and must account for additional hyper-parameters that depend on specific deep architectures, sizes of the pre-training and target datasets, and similarity of domains. However, the similarity of datasets and domains in transfer learning and relatedness of tasks in multi-task learning are difficult to access. Most studies address these limitations by empirical evaluation of the model. Unfortunately, negative results are typically not reported. A deep CNN trained on natural images boosts performance in radiographic images . HoweverIn the medical domain, multimodal, multi-task and transfer learning strategies not only inherit most methodological issues from natural image, text and audio domains, but also pose domain-specific challenges. There is a compelling need for the development of privacy-preserving transfer learning algorithms, such as Private Aggregation of Teacher Ensembles . We sugg6.Deep learning-based methods now match or surpass the previous state of the art in a diverse array of tasks in patient and disease categorization, fundamental biological study, genomics and treatment development. Returning to our central question: given this rapid progress, has deep learning transformed the study of human disease? Though the answer is highly dependent on the specific domain and problem being addressed, we conclude that deep learning has not yet realized its transformative potential or induced a strategic inflection point. Despite its dominance over competing machine learning approaches in many of the areas reviewed here and quantitative improvements in predictive performance, deep learning has not yet definitively ‘solved’ these problems.As an analogy, consider recent progress in conversational speech recognition. Since 2009, there have been drastic performance improvements with error rates dropping from more than 20% to less than 6%  and finaSome of the areas we have discussed are closer to surpassing this lofty bar than others, generally, those that are more similar to the non-biomedical tasks that are now monopolized by deep learning. In medical imaging, diabetic retinopathy , diabetiIn other domains, perfect accuracy will not be required because deep learning will primarily prioritize experiments and assist discovery. For example, in chemical screening for drug discovery, a deep learning system that successfully identifies dozens or hundreds of target-specific, active small molecules from a massive search space would have immense practical value even if its overall precision is modest. In medical imaging, deep learning can point an expert to the most challenging cases that require manual review , though Conversely, the most challenging tasks may be those in which predictions are used directly for downstream modelling or decision-making, especially in the clinic. As an example, errors in sequence variant calling will be amplified if they are used directly for genome-wide association studies. In addition, the stochasticity and complexity of biological systems imply that for some problems, for instance, predicting gene regulation in disease, perfect accuracy will be unattainable.We are witnessing deep learning models achieving human-level performance across a number of biomedical domains. However, machine learning algorithms, including deep neural networks, are also prone to mistakes that humans are much less likely to make, such as misclassification of adversarial examples ,549, a rWe are optimistic about the future of deep learning in biology and medicine. It is by no means inevitable that deep learning will revolutionize these domains, but given how rapidly the field is evolving, we are confident that its full potential in biomedicine has not been explored. We have highlighted numerous challenges beyond improving training and predictive accuracies, such as preserving patient privacy and interpreting models. Ongoing research has begun to address these problems and shown that they are not insurmountable. Deep learning offers the flexibility to model data in its most natural form, for example, longer DNA sequences instead of k-mers for TF binding prediction and molecular graphs instead of pre-computed bit vectors for drug discovery. These flexible input feature representations have spurred creative modelling approaches that would be infeasible with other machine learning techniques. Unsupervised methods are currently less developed than their supervised counterparts, but they may have the most potential because of how expensive and time-consuming it is to label large amounts of biomedical data. If future deep learning algorithms can summarize very large collections of input data into interpretable models that spur scientists to ask questions that they did not know how to ask, it will be clear that deep learning has transformed biology and medicine.7.7.1.We recognized that deep learning in precision medicine is a rapidly developing area. Hence, diverse expertise was required to provide a forward-looking perspective. Accordingly, we collaboratively wrote this review in the open, enabling anyone with expertise to contribute. We wrote the manuscript in markdown and tracked changes using git. Contributions were handled through GitHub, with individuals submitting ‘pull requests’ to suggest additions to the manuscript.To facilitate citation, we defined a markdown citation syntax. We supported citations to the following identifier types (in order of preference): DOIs, PubMed Central IDs, PubMed IDs, arXiv IDs and URLs. References were automatically generated from citation metadata by querying APIs to generate Citation Style Language JSON items for each reference. Pandoc and pandoc-citeproc converted the markdown to HTML and PDF, while rendering the formatted citations and references. In total, referenced works consisted of 372 DOIs, six PubMed Central records, 129 arXiv manuscripts and 48 URLs (webpages as well as manuscripts lacking standardized identifiers).https://greenelab.github.io/deep-review. To ensure a consistent software environment, we defined a versioned conda environment of the software dependencies.We implemented continuous analysis so the manuscript was automatically regenerated whenever the source changed . We confIn addition, we instructed the Travis CI deployment script to perform blockchain timestamping ,553. Usi


Benjamin Xu should not have an Bsc. Xiangyi Kong's degree should have appeared as an MD. Richard Xu should not have an Bsc. Lishun Liu should have an Bsc instead of an MS. Ziyi Zhou should have an Bsc instead of an MS.In the article, “Homocysteine and all-cause mortality in hypertensive adults without pre-existing cardiovascular conditions: Effect modification by MTHFR C677T polymorphism”,


Let  X and Y are said to be negatively quadrant dependent , if Two random variables mentclass2pt{minimI, the r.v.s. A and B of I, and any coordinatewise nondecreasing function G and H with A much stronger concept than LNQD was introduced by Joag-Dev and Proschan : for a fet al. [et al. [et al. [et al. [et al. [et al. [Some applications for LNQD sequence have been found. For example, Newman  establiset al.  studied  [et al.  obtained [et al.  establis [et al.  discusse [et al.  obtained [et al.  obtained [et al.  establis [et al.  establis [et al.  proved tThe main purpose of this paper is to discuss the limit theory for LNQD sequence. In Section C denotes a positive constant, which may take different values whenever it appears in different expressions. We have Throughout the paper, f and g on X and Y satisfying : For any absolutely continuous functions  Now we state the law of iterated logarithm for LNQD sequence.We will need the following property. Letbe a strictly stationary LNQD sequence withandPutThenOur theorem extends the corresponding results of Corollary 1.2 in Choi . Choi esThe proof of Theorem (Lehmann )Let random variables X and Y be NQD, theniffandgare both nondecreasing (or both nonincreasing) functions, thenandare NQD.Letbe an LNQD sequence of random variables with mean zero and finite second moments. LetandThen, for allandwe knowIn particular, we haveBy Lemma Letbe an LNQD sequence of random variables withandDefineandThen, for anywhere Φ is the standard normal distribution function.X be a standard normal random variable and definef be the unique bounded solution of the Stein equationf is given byWe will apply the Stein method. Let ee Stein )2.6\docx, by the definition of LNQD and Lemma For fixed (H1) and ,\documeows from  that\dom be an integer such thatn sufficiently large,n is sufficiently large.It suffices to show that for e Acosta , it is ei. By Lemma n. By using the standard subsequence method, (By the definition of LNQD and Lemma a=2man),  and (2.1a=2man), , we geta.s. Now  follows a.s. Now  and , 2.18)\documenther with  and  for linear processes generated by LNQD sequence with finite variance.e.g., Hannan [et al. [The linear processes are of special importance in time series analysis and they arise in wide variety of concepts (see, , Hannan , Chapter, Hannan  establis, Hannan  proved a, Hannan  obtained, Hannan  establis, Hannan  discusse [et al.  providedLetbe a strictly stationary LNQD sequence withandbe a sequence of real numbers withDefine the linear processesThenThe proof of Theorem Letbe a strictly sequence of random variables, be a monotone decreasing sequence of nonnegative real numbers. ThenLet Letbe a strictly stationary LNQD sequence of random variables withThenm, Let to prove , it is snt as in .B will be given later. Noting the choice of Finally, in order to prove , it remant as of , there ei. By Lemma B sufficiently large such that By the definition of LNQD and Lemma  then by  and (3.1 then by , observi>1. Thus  holds byBy a Beveridge and Nelson decomposition for a linear process, for In this paper, using the Kolmogorov type maximal inequality and Stein’s method, the law of the iterated logarithm for LNQD sequence is established with less restriction of moment conditions, this improves the results of Choi  from \do


This study was conducted to know the status of bovine herpesvirus-1 (BHV-1) antibodies in the bovines of the selected area of Uttarakhand.A total of 489 serum samples, 392 of cattle and 97 of buffaloes were randomly collected from the unvaccinated bovine population of five districts viz., Dehradun, Haridwar, Nainital, Pithoragarh, and Udham Singh Nagar and were tested by avidin biotin enzyme-linked immunosorbent assay for BHV-1 antibodies.The overall prevalence was observed to be 29.03%. At district level, the highest prevalence was recorded in Pithoragarh district (40.00%) while it was lowest in district Udham Singh Nagar (16.00%). The prevalence of BHV-1 antibodies was found to be higher in unorganized dairy units (31.02%) compared to organized farms (26.51%) in Uttarakhand. Buffaloes were found to have greater prevalence (38.14%) than cattle (26.78%) while on sex-wise basis; it was found that more females (30.08%) were harboring antibodies to the virus than males (16.21%).The study revealed that the population in the area under study has been exposed to BHV-1 and hence prevention and control strategies must be implemented. Bovine herpesvirus 1 (BHV-1), which infects domestic and range cattle, is associated with several clinical conditions including infectious bovine rhinotracheitis (IBR), infectious pustular vulvovaginitis, balanoposthitis, conjunctivitis, and generalized disease in newborn calves. BHV-1 has been classified into subtypes 1.1, 1.2a, and 1.2b using restriction enzyme analysis. Subtype 1.1 is associated with respiratory disease while subtypes 1.2a and 1.2b are implicated in infectious pustular vulvovaginitis/infectious balanoposthitis syndrome and cause mild respiratory disease [The virus is transmitted primarily through aerosol or genital contact. Clinical signs are generally mild, and the virus does not cause high mortality, infection results in a latent state and lifelong infection. Reactivation of latent BHV-1 infections can occur due to corticosteroid treatment or stress due to transportation, overcrowding or adverse weather conditions. The productivity and reproductivity of the animals is greatly decreased as an outcome of the disease .Various workers have published reports regarding the prevalence of this disease from different parts of India -7.Enzyme-linked immunosorbent assay (ELISA) is a sensitive and specific test in terms of detection of the low level of antibody for several viral diseases; this has been extensively used in recent last by many workers to monitor the seroprevalence of IBR in cattle population . There aThis investigation was carried out to know the prevalence of BHV-1 antibodies in bovines of Uttarakhand using avidin biotin ELISA (AB-ELISA) and to determine the significance of risk factors such as management, species, and sex associated with the BHV-1 prevalence.As per CPCSEA guidelines, a study involving clinical samples does not require the approval of Institute Animal Ethics Committee.viz., Dehradun, Haridwar, Nainital, Pithoragarh, and Udham Singh Nagar . BHV-1 antigen-coated plate was brought to room temperature. 100 µl of diluted control and test sera were added in duplicates to the wells. The plate was incubated on shaker for 1 h at 37°C. Thereafter was washed 3 times with washing buffer (50 µl of Tween 20 to 100 ml of PBS ×1). Then, 100 µl of biotin anti-immunoglobulin G conjugate diluted 1:30000 in blocking buffer was added to all the wells. Plate was again incubated on shaker for 1 h at 37°C and washed 3 times with washing buffer. 100 µl of Avidin-horseradish peroxidase conjugate diluted to 1:20,000 in blocking buffer was added to each well. Plate was again incubated on shaker for 20 min at 37°C and then washed 3 times with washing buffer. Then, 100 µl of chromogen/substrate was added to all the wells. The plate was then incubated at room temperature for 8-10 min. Then, 50 µl of stopping solution (1 N sulfuric acid) was added to all the wells. The plate was read in the ELISA plate reader at 492 nm. Results of test sera were expressed as percent positivity (PP) values calculated as shown below:PP = (Average OD of sample/Average OD of strong serum positive) × 100The test sera with PP values ≥45 were considered positive for IBR antibodies positive.Statistical analysis of data was performed as per the method described by Snedecor and Cochran .Out of 489 serum samples screened, 142 (29.03%) were found to be positive AB-ELISA for BHV-1 antibodies . At distSeroprevalence was higher in unorganized dairy units (31.02%) compared to organized farms (26.51%) in Uttarakhand. At species level also higher prevalence level was observed in both cattle and buffalo from unorganized sector compared to those from organized sector. However, the dependency of prevalence on management system followed was nonsignificant at p≤0.05. Species wise seroprevalence was significantly higher in buffaloes (38.14%) than cattle (26.78%) at p≤0.05. Overall, sex-wise seroprevalence indicated that more females (30.08%) were affected by BHV-1 than males (16.21%). Dependency of prevalence on sex was nonsignificant at p≤0.05. At species level also for prevalence percentage in both cattle and buffaloes was more in females  than males .et al. [et al. [et al. [et al. [In Uttarakhand, in earlier studies, Jain et al.  and Nand [et al.  have rep [et al.  have als [et al. . The difet al. [viz., Udham Singh Nagar, Dehradun, and Pithoragarh. Although the prevalence of different districts differed significantly, however, there is a need to study in detail the other factors such as migration of animal, breeding practices, geographical location, and climatic conditions and their effect on virus spread before associating the occurrence on antibodies in particular district. Much higher prevalence rates from different parts of India and world were reported by various workers [et al. [et al. [et al. [In agreement with this study, Thakur et al.  also rep workers -20. Wher [et al. , Das et  [et al. , Singh a [et al. , and Sin [et al.  from difet al. [et al. [et al. [et al. [et al. [et al. [Similar to our observation Rajesh et al.  in Keralet al.  observed [et al.  also com [et al.  and Sath [et al.  also rep [et al. . Gonzale [et al.  indicateet al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [Similar to our observations, on species wise prevalence, other researchers have reported greater prevalence in buffaloes than cattle. In Uttarakhand, Thakur et al.  have als [et al.  who also [et al. , Nandi e [et al. , and Tra [et al.  in India [et al.  and Jain [et al.  in indep [et al.  in Gujar [et al.  in Uttar [et al.  in Uttaret al. [et al. [et al. [et al. [et al. [et al. [et al. [Observations of this study in context to sex wise prevalence were in agreement to study of Thakur et al.  who have [et al.  in Uttar [et al.  also obs [et al. ; Krishna [et al.  in south [et al.  in Bihar [et al.  in Uttar [et al. . Contrar [et al.  had obseThis study revealed that the BHV-1 is strengthening its roots in different parts of Uttarakhand. The present findings warrant that a suitable action plan should be implemented to control the spread of virus in the state.A collection of serum samples, screening of samples by ELISA was done by VT. The entire work was done under supervision of MK. Data were analyzed by VT. The manuscript was prepared by VT, and corrections/modifications were done by MK and RRL. All authors read and approved the manuscript.