Create README.md

README.md

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+---
+license: afl-3.0
+tags:
+- biology
+---
+
+FLIP AAV Dataset Splits
+The AAV (Adeno-Associated Virus) dataset in FLIP focuses on predicting the fitness/efficiency of AAV capsid variants for gene therapy.
+
+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.
+
+- low_vs_high
+  - Training set: Sequences with fitness values equal to or below wild type
+  - Test set: Sequences with fitness values above wild type
+  - Purpose: Tests if models can extrapolate from low-performing sequences to predict high-performing ones
+
+- one_vs_many
+  - Training set: Sequences with exactly 1 mutation from wild type
+  - Test set: Sequences with many mutations (more than 1)
+  - Purpose: Tests generalization from single mutants to multi-mutant sequences
+
+- two_vs_many
+  - Training set: Sequences with ≤2 mutations from wild type
+  - Test set: Sequences with more than 2 mutations
+  - Purpose: Tests if models trained on low-mutation sequences can predict fitness of higher-mutation sequences
+
+- seven_vs_many
+  - Training set: Sequences with exactly 7 mutations from wild type
+  - Test set: Sequences with a different number of mutations
+  - Purpose: Tests generalization when training on a specific mutation count
+
+- des_mut (Designed vs Mutant)
+  - Training set: Designed sequences (rationally designed variants)
+  - Test set: Random mutants
+  - Purpose: Tests if models trained on designed sequences can predict fitness of random mutants
+
+- mut_des (Mutant vs Designed)
+  - Training set: Random mutants
+  - Test set: Designed sequences
+  - Purpose: The reverse - tests if models trained on random mutants can predict designed sequence fitness
+
+- sampled
+  - Random 80/20 train-test split
+  - Used as a baseline comparison