Datasets:
| { | |
| "READY": true, | |
| "name": "true_fluorescence", | |
| "task": "regression", | |
| "cat_idx": [], | |
| "num_samples": 51738, | |
| "num_features": 3068, | |
| "description": "Sequence-level regression task predicting the log-fluorescence of higher-order mutant green fluorescent protein (avGFP) sequences. The library was generated via random mutagenesis of the wildtype sequence. Training is restricted to sequences with three or fewer mutations from parent GFP sequences; the test set contains sequences with four or more mutations, following the TAPE and PEER benchmarks. A random maximal subset of non-degenerate coding sequences was selected. The source provides coding DNA sequences (CDS); they were translated with the standard genetic code before featurization, and nonsense mutants (CDS with a premature in-frame stop codon) were removed from all splits. Features: TF-IDF of amino-acid 3-mers of the translated sequence; vocabulary (3-mers in >= 2 training sequences, top 8,000), an extra '<unk>' term for all other 3-mers, and IDF weights are fitted on the training split.", | |
| "source": "InstaDeepAI/true-cds-protein-tasks (Hugging Face). Original data from Sarkisyan et al. \u2014 experimental study of the avGFP fitness landscape via random mutagenesis synthesized in E. coli. Split follows TAPE/PEER: train on sequences with \u22643 mutations from parent GFP, test on sequences with \u22654 mutations.", | |
| "label": "Proteomics", | |
| "sub_labels": [ | |
| "Protein sequence", | |
| "Translated CDS" | |
| ] | |
| } |