---
license: afl-3.0
tags:
- biology
---


FLIP Meltome Dataset Explanation
What is the Meltome Dataset?
The Meltome dataset is a thermostability prediction dataset derived from the Meltome Atlas, a large-scale study that measured the melting temperatures (Tm) of proteins across the tree of life.


Note: DROP any example that has split=nan when training. The reason for leaving them is to keep this dataset identical to the original one.



1. mixed_split.csv
  - Purpose: Cross-species diversity split
  - Description: Uses MMseqs2 clustering with >20% sequence identity
  - Clusters proteins at 20% identity threshold
  - 80% of clusters → training set
  - 20% of clusters → test set
  - Goal: Avoid information leakage between train and test (ensures proteins in test are sufficiently different from training)
  - This tests the model's ability to generalize across diverse protein families and species

2. human.csv
  - Purpose: Human-specific proteins
  - Description: Contains only protein sequences from humans
  - Likely includes data from multiple human cell lines, tissues, and body fluids
  - The Meltome Atlas includes human TPP data from 14 cell lines, primary cells, tissues, and 5 body fluids


3. human_cell.csv
  - Purpose: Human cellular proteins
  - Description: Subset of human proteins specifically from intact cells (not lysates)
  - The Meltome Atlas distinguishes between:


Cell-based measurements can show different Tm values due to cellular context effects

Labels/Target Variable
Each protein sequence has an associated Tm value (in °C):

This is a regression task - you predict the continuous melting temperature
Example: A protein might have Tm = 55.3°C

Why This Dataset Matters

Protein engineering: Thermostable proteins are valuable for:
Industrial enzymes (operate at higher temperatures)
Pharmaceutical development
Starting points for directed evolution


Machine learning challenge:
Tests whether models can predict thermostability from sequence alone
Cross-species prediction is particularly challenging
Models must learn what sequence features confer thermal stability



Key Challenge
Research has shown that models achieving high Spearman correlation on cross-species data (mixed_split) may be misleadingly good - they often learn to distinguish global amino acid composition differences between species rather than the specific sequence features that determine thermostability within a species.
This is why having both mixed_split (cross-species) and human / human_cell (species-specific) splits is important for proper evaluation.