{"count":3,"items":[{"article":null,"cancer_slugs":["pan-cancer"],"category":"genomics","confidence":"high","developer":"Theodoris Lab (Gladstone Institutes / UCSF)","hf":{"downloads":1698,"fetched_at":"2026-09-09T21:33:08Z","gated":false,"last_modified":"2026-05-26","library":"transformers","license":"apache-2.0","likes":311,"pipeline_tag":"fill-mask"},"kind":"foundation","license":"apache-2.0","links":{"demo":null,"docs":null,"doi":"10.1038/s41586-023-06139-9","github":null,"huggingface":"https://huggingface.co/ctheodoris/Geneformer","paper":"https://www.nature.com/articles/s41586-023-06139-9","pmid":null},"modalities":["single-cell","transcriptomics"],"name":"Geneformer","openness":"open-weights","regulatory_status":"not-applicable","release_date":"2023-05-31","settings":["basic-research","drug-discovery"],"slug":"geneformer","summary":"Transformer pretrained on about 30 million single-cell transcriptomes (Genecorpus-30M) that learns gene-network context from ranked expression, enabling few-shot prediction of gene dosage effects, cell states and candidate therapeutic targets.","tasks":["single-cell","gene-expression","feature-extraction","drug-discovery"],"url":"/ai-oncology/models/geneformer","verified_at":"2026-09-07T07:05:05.599962","version":null},{"article":"/blog/modele-prezentacji-antygenu","cancer_slugs":["pan-cancer"],"category":"immunopeptidomics","confidence":"high","developer":"Broad Institute / Dana-Farber Cancer Institute (Keskin, Wu, Carr labs)","hf":null,"kind":"task-model","license":"free web server; academic use","links":{"demo":"http://hlathena.tools/","docs":null,"doi":"10.1038/s41587-019-0322-9","github":null,"huggingface":null,"paper":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7008090/","pmid":null},"modalities":["protein-sequence","immunopeptidomics","transcriptomics"],"name":"HLAthena","openness":"api-only","regulatory_status":"research-only","release_date":"2020-01-13","settings":["basic-research","vaccine-design","immunotherapy"],"slug":"hlathena","summary":"Presentation predictor trained on the mono-allelic peptidome of 95 cell lines \u2014 186,464 peptides across 95 HLA alleles, fifteen of which had no described motif before \u2014 which is the dataset that changed this field more than any architectural idea.","tasks":["antigen-presentation","peptide-mhc-binding","neoantigen-prioritisation"],"url":"/ai-oncology/models/hlathena","verified_at":"2026-09-05T22:26:00.948597","version":null},{"article":null,"cancer_slugs":["pan-cancer"],"category":"genomics","confidence":"high","developer":"Bo Wang Lab, University of Toronto","hf":null,"kind":"foundation","license":"MIT","links":{"demo":null,"docs":null,"doi":"10.1038/s41592-024-02201-0","github":"https://github.com/bowang-lab/scGPT","huggingface":null,"paper":"https://www.nature.com/articles/s41592-024-02201-0","pmid":null},"modalities":["single-cell","transcriptomics"],"name":"scGPT","openness":"open-weights","regulatory_status":"not-applicable","release_date":"2024-02-08","settings":["basic-research"],"slug":"scgpt","summary":"Generative pretrained transformer for single-cell multi-omics, trained on over 33 million cells, supporting cell-type annotation, batch integration, perturbation response prediction and gene-network inference.","tasks":["single-cell","gene-expression","feature-extraction"],"url":"/ai-oncology/models/scgpt","verified_at":"2026-09-05T22:26:01.064048","version":null}],"standard":"https://cancer3.ai/docs/ai-model-card","vocab":{"confidence":["high","medium","low"],"dataset_access":["open","registration","dua","controlled","commercial"],"dataset_kinds":["dataset","registry","benchmark","biobank","corpus"],"formats":["SVS","TIFF","NDPI","DICOM","NIfTI","PNG","JPEG","CSV","TSV","JSON","Parquet","FASTQ","BAM","VCF","MAF","h5ad","PDB","mmCIF","text","other"],"kinds":["foundation","task-model","tool","framework","product"],"link_roles":["pretraining","training","finetuning","evaluation","benchmark"],"modalities":["histopathology","radiology-ct","radiology-mri","radiology-xray","mammography","pet","ultrasound","dermoscopy","endoscopy","genomics","transcriptomics","single-cell","proteomics","protein-sequence","molecules","clinical-text","literature","ehr","multimodal","immunopeptidomics"],"need_status":["open","in-progress","solved"],"openness":["open-weights","gated-weights","api-only","closed"],"regulatory":["research-only","fda-cleared","fda-de-novo","fda-pma","ce-marked","ce-ivdr","not-applicable"],"settings":["basic-research","screening","diagnosis","prognosis","treatment-planning","treatment-response","drug-discovery","clinical-trials","education","vaccine-design","immunotherapy"],"tasks":["feature-extraction","classification","segmentation","detection","screening","risk-prediction","prognosis","treatment-response","survival-analysis","image-text-retrieval","report-generation","question-answering","information-extraction","clinical-trial-matching","protein-structure","variant-effect","gene-expression","single-cell","drug-discovery","drug-repurposing","peptide-mhc-binding","antigen-presentation","neoantigen-prioritisation"]}}
