Datasets:

hazemessam
/

aav

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