Omkar2118/datasets huggingface dataset:
FLIP
Full name: Fitness Landscape Inference for Proteins

FLIP includes several benchmark datasets that contain a variety of protein sequences, each with a real-valued label indicating its "fitness" (how well the protein performs some particular function). The goal is to predict the fitness of a given protein sequence using the sequence. Different representations of protein sequences (e.g. learned embeddings from large language models) may prove helpful here.

Some of the benchmark datasets (thermostability) contain a highly diverse set of sequences from many different protein families. Others (AAV, GB1) contain all sequences that are mutants of a single parent sequence. Each benchmark dataset features multiple "splits" -- different ways of train-test splitting the data to assess how well a model might generalize given limited information. The AAV benchmark, for example, features the "mutant vs designed" split in which a model is trained on randomly generated mutants and asked to predict the fitness of designed sequences, and the "seven vs many" split in which a model is trained on sequences with seven mutations and asked to make predictions for sequences with a different number of mutations.

Introducing paper: FLIP: Benchmark tasks in fitness landscape inference for proteins
FLIP -- AAV, Designed vs mutant
Full name: adeno-associated virus

FLIP includes several benchmark datasets that contain a variety of protein sequences, each with a real-valued label indicating its "fitness" (how well the protein performs some particular function). The goal is to predict the fitness of a given protein sequence using the sequence. Different representations of protein sequences (e.g. learned embeddings from large language models) may prove helpful here.

This sub-dataset (AAV) is a set of 201,426 training sequences and 82,583 test sequences in which the goal is to predict the fitness of mutants of the capsid protein from the adeno-associated virus (AAV). The training set proteins were designed, while the test set proteins are random mutants. The absolute value of the fitness is not important, but its ranking / relative value is -- protein designers would like to be able to pick a sequence with high fitness relative to those in the training set. Performance is therefore usually assessed using Spearman's r correlation coefficient.

Introducing paper: FLIP: Benchmark tasks in fitness landscape inference for proteins