| --- |
| license: cc-by-4.0 |
| pretty_name: true_fluorescence |
| task_categories: |
| - tabular-regression |
| tags: |
| - tabular |
| - biomedical |
| - benchmark |
| - TabBioMed |
| - proteomics |
| - regression |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.parquet |
| - split: validation |
| path: data/val.parquet |
| - split: test |
| path: data/test.parquet |
| --- |
| |
| # true_fluorescence |
| |
| Part of **TabBioMed** (*TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning*), a benchmark of 94 curated biomedical |
| tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed. |
| |
| 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. |
| |
| | Domain | Task | Samples | Features | Classes | |
| |---|---|---|---|---| |
| | Proteomics | regression | 51,738 | 3,068 | - | |
| |
| **Splits.** Fixed train/val/test split (20968/5252/25518 rows). |
| |
| ## Files |
| |
| - `data/**.parquet`: one row per sample. `row_idx` is the row index in the original |
| TabBioMed array; `f_<i>[_<name>]` are the features (in order); `target` (or `target_<j>` |
| for multi-target tasks) is the label. Categorical feature indices (0-based, among the |
| features): `[]`. Missing values are stored as nulls. |
| - `metadata.json`: TabBioMed task metadata (task type, categorical indices, source, description). |
| - `feature_names.json`: original feature names, in column order. |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("TabBioMed/true_fluorescence") |
| ``` |
| |
| A Croissant (1.1) description with Responsible-AI fields is distributed with the |
| TabBioMed release. |
| |
| ## Source and license |
| |
| Source: InstaDeepAI/true-cds-protein-tasks (Hugging Face). Original data from Sarkisyan et al. — experimental study of the avGFP fitness landscape via random mutagenesis synthesized in E. coli. Split follows TAPE/PEER: train on sequences with ≤3 mutations from parent GFP, test on sequences with ≥4 mutations. |
| |
| This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also |
| cite and respect the terms of the original source. |
| |
| ## Citation |
| |
| ```bibtex |
| @inproceedings{tabbiomed2026, |
| title = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning}, |
| author = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, |
| year = {2026} |
| } |
| ``` |
| |
