TabBioMed camera-ready release

README.md

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+---
+license: cc-by-4.0
+pretty_name: vep_pathogenic_coding
+task_categories:
+- tabular-classification
+tags:
+- tabular
+- biomedical
+- benchmark
+- TabBioMed
+- genomics
+- binary
+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
+---
+
+# vep_pathogenic_coding
+
+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.
+
+Classify coding single-nucleotide variants as pathogenic (ClinVar, label 1) or common (gnomAD MAF > 5%, label 0) from DNA sequence context only. Variants are strand-canonicalised (reverse-complemented when the reference allele is a purine, so it is always C or T). Features: GRCh38 reference bases at offsets -32..+32 around the variant (the central base is the reference allele) and the alternative allele (66 categorical), plus canonical 3-mer frequencies (k-mer and reverse complement merged) of the 513-bp reference window centred on the variant (32 numeric). Allele-frequency fields, annotation fields (VEP consequence, ClinVar IDs/review status) and genomic coordinates are excluded because they define or reveal the label. chrX/chrY variants (all pathogenic by construction) and two variants listed with both labels are removed. Split by whole chromosomes: test = chr1, chr4, chr21; validation = chr5, chr6, chr11; train = remaining autosomes.
+
+| Domain | Task | Samples | Features | Classes |
+|---|---|---|---|---|
+| Genomics | binary | 37,166 | 98 | 2 |
+
+**Splits.** Fixed train/val/test split (26056/5766/5344 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): `[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65]`. 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/vep_pathogenic_coding")
+```
+
+A Croissant (1.1) description with Responsible-AI fields is distributed with the
+TabBioMed release.
+
+## Source and license
+
+Source: InstaDeepAI/genomics-long-range-benchmark (Hugging Face), task variant_effect_pathogenic (ClinVar, coding SNVs): ClinVar pathogenic variants vs gnomAD v3.1.2 common variants (MAF > 5%, AN >= 25,000) (Kao et al., 2024). Sequence context from the UCSC hg38 (GRCh38) reference genome.
+
+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}
+}
+```

data/test.parquet

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+oid sha256:e37b7f270132b651a9c1cdda685a753b8feffdc1cd03d8bf74a583eb7b891840
+size 311413

data/train.parquet

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+size 1281957

data/val.parquet

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+oid sha256:273e33404e8f11b7fa3d74644b3cdaee6b07c255fd66abdbba86a3630393bbb0
+size 331701

feature_names.json

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+[
+  "ref_-32",
+  "ref_-31",
+  "ref_-30",
+  "ref_-29",
+  "ref_-28",
+  "ref_-27",
+  "ref_-26",
+  "ref_-25",
+  "ref_-24",
+  "ref_-23",
+  "ref_-22",
+  "ref_-21",
+  "ref_-20",
+  "ref_-19",
+  "ref_-18",
+  "ref_-17",
+  "ref_-16",
+  "ref_-15",
+  "ref_-14",
+  "ref_-13",
+  "ref_-12",
+  "ref_-11",
+  "ref_-10",
+  "ref_-9",
+  "ref_-8",
+  "ref_-7",
+  "ref_-6",
+  "ref_-5",
+  "ref_-4",
+  "ref_-3",
+  "ref_-2",
+  "ref_-1",
+  "ref_0",
+  "ref_+1",
+  "ref_+2",
+  "ref_+3",
+  "ref_+4",
+  "ref_+5",
+  "ref_+6",
+  "ref_+7",
+  "ref_+8",
+  "ref_+9",
+  "ref_+10",
+  "ref_+11",
+  "ref_+12",
+  "ref_+13",
+  "ref_+14",
+  "ref_+15",
+  "ref_+16",
+  "ref_+17",
+  "ref_+18",
+  "ref_+19",
+  "ref_+20",
+  "ref_+21",
+  "ref_+22",
+  "ref_+23",
+  "ref_+24",
+  "ref_+25",
+  "ref_+26",
+  "ref_+27",
+  "ref_+28",
+  "ref_+29",
+  "ref_+30",
+  "ref_+31",
+  "ref_+32",
+  "alt",
+  "k3_w513_AAA",
+  "k3_w513_AAC",
+  "k3_w513_AAG",
+  "k3_w513_AAT",
+  "k3_w513_ACA",
+  "k3_w513_ACC",
+  "k3_w513_ACG",
+  "k3_w513_ACT",
+  "k3_w513_AGA",
+  "k3_w513_AGC",
+  "k3_w513_AGG",
+  "k3_w513_ATA",
+  "k3_w513_ATC",
+  "k3_w513_ATG",
+  "k3_w513_CAA",
+  "k3_w513_CAC",
+  "k3_w513_CAG",
+  "k3_w513_CCA",
+  "k3_w513_CCC",
+  "k3_w513_CCG",
+  "k3_w513_CGA",
+  "k3_w513_CGC",
+  "k3_w513_CTA",
+  "k3_w513_CTC",
+  "k3_w513_GAA",
+  "k3_w513_GAC",
+  "k3_w513_GCA",
+  "k3_w513_GCC",
+  "k3_w513_GGA",
+  "k3_w513_GTA",
+  "k3_w513_TAA",
+  "k3_w513_TCA"
+]

metadata.json

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+{
+  "READY": true,
+  "name": "vep_pathogenic_coding",
+  "task": "binary",
+  "num_classes": 2,
+  "cat_idx": [
+    0,
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20,
+    21,
+    22,
+    23,
+    24,
+    25,
+    26,
+    27,
+    28,
+    29,
+    30,
+    31,
+    32,
+    33,
+    34,
+    35,
+    36,
+    37,
+    38,
+    39,
+    40,
+    41,
+    42,
+    43,
+    44,
+    45,
+    46,
+    47,
+    48,
+    49,
+    50,
+    51,
+    52,
+    53,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    60,
+    61,
+    62,
+    63,
+    64,
+    65
+  ],
+  "num_samples": 37166,
+  "num_features": 98,
+  "imbalance_ratio": 1.1365,
+  "description": "Classify coding single-nucleotide variants as pathogenic (ClinVar, label 1) or common (gnomAD MAF > 5%, label 0) from DNA sequence context only. Variants are strand-canonicalised (reverse-complemented when the reference allele is a purine, so it is always C or T). Features: GRCh38 reference bases at offsets -32..+32 around the variant (the central base is the reference allele) and the alternative allele (66 categorical), plus canonical 3-mer frequencies (k-mer and reverse complement merged) of the 513-bp reference window centred on the variant (32 numeric). Allele-frequency fields, annotation fields (VEP consequence, ClinVar IDs/review status) and genomic coordinates are excluded because they define or reveal the label. chrX/chrY variants (all pathogenic by construction) and two variants listed with both labels are removed. Split by whole chromosomes: test = chr1, chr4, chr21; validation = chr5, chr6, chr11; train = remaining autosomes.",
+  "source": "InstaDeepAI/genomics-long-range-benchmark (Hugging Face), task variant_effect_pathogenic (ClinVar, coding SNVs): ClinVar pathogenic variants vs gnomAD v3.1.2 common variants (MAF > 5%, AN >= 25,000) (Kao et al., 2024). Sequence context from the UCSC hg38 (GRCh38) reference genome.",
+  "label": "genomics"
+}