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| "name": "BenchRep-T", |
| "description": "BenchRep-T is a benchmark for TCRβ (T-cell receptor β-chain) immune-repertoire classification. It bundles the multi-disease Mal-ID cohort together with five external disease cohorts (sequenced on the Adaptive Biotechnologies immunoSEQ platform: Savola_RA, Musvosvi_TB, Rawat_T1D, Mitchell_T1D, Emerson_CMV), providing per-specimen repertoire files, harmonized sample metadata, and auxiliary files used for the sequencing-depth scaling-law and driver-sequence experiments. Each specimen (one sequenced repertoire) is one example; the classification target is the specimen-level disease label. See the full description at https://huggingface.co/datasets/lordim/BenchRep-T.", |
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| "name": "Alejandro Buendia" |
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| "name": "Chiho Im" |
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| "name": "Anshul Kundaje" |
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| "citeAs": "@inproceedings{buendia2026benchrep,\n title = {BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics},\n author = {Buendia, Alejandro and Im, Chiho and Kundaje, Anshul and Boyd, Scott D and Cohen-Lavi, Liel},\n booktitle = {Advances in Neural Information Processing Systems, Evaluations and Datasets Track},\n year = {2026}\n}", |
| "keywords": [ |
| "mit", |
| "1K<n<10K", |
| "Region: US", |
| "immunology", |
| "t-cell-receptor", |
| "tcr-beta", |
| "immune-repertoire", |
| "AIRR", |
| "immunoseq", |
| "repertoire-classification" |
| ], |
| "license": "https://choosealicense.com/licenses/mit/", |
| "url": "https://huggingface.co/datasets/lordim/BenchRep-T", |
| "dataCollection": "Bulk TCRβ immune-repertoire sequencing aggregated from previously published, de-identified human cohorts. Mal-ID is multi-disease B/T-cell receptor sequencing (Zaslavsky et al. 2025); the five external cohorts are Adaptive Biotechnologies immunoSEQ TCRβ repertoires from published disease studies: Savola et al. 2017 (rheumatoid arthritis), Musvosvi et al. 2023 (tuberculosis), Rawat et al. 2026 and Mitchell et al. 2022 (type 1 diabetes), and Emerson et al. 2017 (cytomegalovirus).", |
| "dataCollectionType": "Secondary aggregation and harmonization of previously published, de-identified human immune-repertoire sequencing datasets.", |
| "dataCollectionTimeframe": "Source studies published 2017–2026.", |
| "dataPreprocessingProtocol": "Repertoire files were harmonized into per-specimen rearrangement tables (AIRR-style for Mal-ID; Adaptive immunoSEQ exports for the external cohorts) and gzip-compressed. Specimen-level disease labels and cross-validation fold assignments are provided in per-cohort metadata.tsv files. Specimens without a usable label were excluded (e.g., 66 BCR-only specimens dropped from Mal-ID; 25 unknown-CMV-status specimens dropped from Emerson_CMV).", |
| "dataAnnotationProtocol": "Specimen-level disease/status labels are inherited from the clinical annotations of the original source studies; no additional manual annotation was performed for this benchmark.", |
| "dataUseCases": "Intended for benchmarking machine-learning methods that classify disease or exposure status from an individual's TCRβ immune repertoire. Not intended for clinical diagnosis or individual-level medical decision-making.", |
| "dataBiases": "Cohorts differ in demographic composition, recruitment, sequencing platform and depth, and case/control balance. Because labels come from heterogeneous studies, models may learn cohort- or platform-specific batch effects rather than disease biology, and performance may not transfer across cohorts or to underrepresented populations.", |
| "dataLimitations": "Each cohort comprises a modest number of specimens (91–761). Only the TCRβ chain is provided (no paired α/β chains and no BCR). Mal-ID uses a different assay from the immunoSEQ external cohorts, limiting cross-cohort comparability. Demographic/clinical covariates are limited and vary by cohort.", |
| "dataSocialImpact": "Repertoire-based classifiers could support non-invasive diagnostics, but deploying models that underperform on underrepresented demographic groups risks inequitable outcomes; this dataset is intended for research benchmarking only.", |
| "personalSensitiveInformation": "Data consist of de-identified human immune-repertoire sequences plus a limited set of demographic and clinical fields (e.g., age, sex, ancestry/race, and HLA typing or autoantibody status for some cohorts). No direct identifiers are included. Note that immune-repertoire and HLA data can in principle be re-identifying; use in accordance with the data-use terms of the original source studies.", |
| "dataReleaseMaintenancePlan": "Released as a static benchmark snapshot under the MIT License via the Hugging Face Hub; any updates are tracked through the repository commit history." |
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|
|
