|
|
--- |
|
|
license: afl-3.0 |
|
|
tags: |
|
|
- biology |
|
|
--- |
|
|
|
|
|
|
|
|
FLIP SCL Dataset: SubCellular Localization |
|
|
SCL = SubCellular Localization |
|
|
What It Is |
|
|
This is a multi-label classification task where the goal is to predict which cellular compartment(s) a protein resides in within eukaryotic cells. |
|
|
The 10 Subcellular Locations |
|
|
|
|
|
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. |
|
|
|
|
|
|
|
|
Based on the DeepLoc dataset that FLIP appears to be derived from: |
|
|
|
|
|
Nucleus |
|
|
Cytoplasm |
|
|
Extracellular (secreted proteins) |
|
|
Mitochondrion |
|
|
Cell membrane |
|
|
Endoplasmic reticulum (ER) |
|
|
Chloroplast (plants only) |
|
|
Golgi apparatus |
|
|
Lysosome/Vacuole |
|
|
Peroxisome |
|
|
|
|
|
Why Multi-Label? |
|
|
Some proteins localize to multiple compartments (e.g., shuttling between nucleus and cytoplasm, or dual-targeted to mitochondria and chloroplasts). This is biologically important - around 20-30% of proteins have multiple localizations. |
|
|
The Splits Explained |
|
|
Looking at the directory structure, FLIP provides these splits: |
|
|
|
|
|
1. balanced.csv |
|
|
Standard split with balanced representation across localization categories |
|
|
Avoids class imbalance issues |
|
|
Likely uses random or cluster-based splitting |
|
|
|
|
|
2. human_hard.csv / human_soft.csv |
|
|
Human-only proteins (Homo sapiens) |
|
|
Hard split: More challenging - possibly using lower sequence identity cutoff between train/test, or testing on rare/underrepresented localizations |
|
|
Soft split: Easier - higher sequence similarity allowed between train/test sets |
|
|
|
|
|
3. mixed_hard.csv / mixed_soft.csv |
|
|
Cross-species proteins (eukaryotes: yeast, plants, animals, fungi) |
|
|
Hard split: Tests generalization across phylogenetically distant organisms or rare localization patterns |
|
|
Soft split: Tests on more similar proteins/organisms |
|
|
|
|
|
What Makes Splits "Hard" vs "Soft"? |
|
|
Based on subcellular localization literature: |
|
|
|
|
|
Soft splits likely have: |
|
|
Higher sequence identity between train/test (e.g., 30-40% homology allowed) |
|
|
Similar organisms in train/test |
|
|
Balanced representation of all localization types |
|
|
|
|
|
Hard splits likely have: |
|
|
Lower sequence identity between train/test (e.g., <20-30% homology) |
|
|
Phylogenetic distance: Train on yeast/bacteria, test on human |
|
|
Rare localizations: Test on underrepresented compartments (peroxisome, chloroplast) |
|
|
Multi-localization complexity: Test on proteins with multiple localizations |
|
|
|
|
|
Key Challenge for FLIP |
|
|
Unlike other FLIP tasks that test extrapolation in fitness values (e.g., low→high fitness), SCL tests: |
|
|
Sequence-based generalization: Can models predict localization for proteins unlike any in training? |
|
|
Multi-label complexity: Handling proteins that go to 2-3 compartments |
|
|
Cross-species transfer: Learning universal localization rules vs. species-specific signals |
|
|
|
|
|
|
