| | --- |
| | 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. |
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| | 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. |
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| | - 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 |
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| | - 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 |
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| | - 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 |
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| | - 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 |
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| | - 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 |
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| | - 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 |
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| | - sampled |
| | - Random 80/20 train-test split |
| | - Used as a baseline comparison |
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