Create README.md

README.md

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+---
+license: afl-3.0
+---
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+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.
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+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
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+
+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
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+
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+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.