| { |
| "READY": true, |
| "name": "true_melting_point", |
| "task": "regression", |
| "cat_idx": [], |
| "num_samples": 12144, |
| "num_features": 480, |
| "description": "Sequence-level regression task predicting the melting temperature of proteins. Data originates from the Thermostability Atlas, compiled via a mass spectrometry-based proteomic approach. Splits follow the 'mixed' strategy from FLIP: sequences are clustered at 20% identity, with 80% of clusters assigned to train and 20% to test, avoiding over-emphasis of large clusters. Features are mean-pooled ESM-2 (esm2_t12_35M_UR50D) sequence embeddings.", |
| "source": "InstaDeepAI/true-cds-protein-tasks (Hugging Face). Original data from the Thermostability Atlas (mass spectrometry-based proteomics). Splits follow the 'mixed' split strategy from FLIP: sequences clustered at 20% identity, 80% of clusters assigned to train and 20% to test.", |
| "label": "Proteomics", |
| "sub_labels": [ |
| "CDS-seq" |
| ] |
| } |
