{"id":"W4408398233","doi":"10.1038/s41592-024-02563-5","title":"Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage","year":2025,"lang":"en","type":"article","venue":"Nature Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Common Fund; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Allergy and Infectious Diseases; National Human Genome Research Institute; National Institute of Diabetes and Digestive and Kidney Diseases; Canadian Institute for Advanced Research; NIH Office of the Director; National Heart, Lung, and Blood Institute; National Institute on Aging; National Cancer Institute; U.S. Department of Health and Human Services; National Institutes of Health; U.S. Department of Veterans Affairs","keywords":"Atlas (anatomy); Computer science; Workflow; Human Protein Atlas; Terminology; Annotation; Artificial intelligence; Database; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006554937,0.001815023,0.001643291,0.0069601,0.001373833,0.003623885,0.00356715,0.001559816,0.05424945],"category_scores_gemma":[0.01311145,0.001607118,0.001936043,0.009214989,0.000691974,0.002547959,0.005913431,0.002205117,0.03280639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001593628,"about_ca_system_score_gemma":0.004621974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01140415,"about_ca_topic_score_gemma":0.01079496,"domain_scores_codex":[0.9981975,0.0005434104,0.0002099744,0.0004108445,0.0004704512,0.0001677914],"domain_scores_gemma":[0.996185,0.001282036,0.0003577401,0.001057738,0.0008159368,0.0003015093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005011717,0.00004611762,0.002687635,0.002168414,0.0003082645,0.0002952087,0.0007056406,0.003034224,0.005037812,0.01912807,0.8649762,0.1011114],"study_design_scores_gemma":[0.0001714899,0.00006259923,0.007373157,0.0004806508,0.0002279287,0.0007172387,0.0001950947,0.004691122,0.008151124,0.02655852,0.9512347,0.0001365023],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.004578426,0.002229687,0.3218916,0.001620147,0.0005996093,0.0008866203,0.5172194,0.1262451,0.02472949],"genre_scores_gemma":[0.02111236,0.002319814,0.3904111,0.0009039182,0.0002316187,0.004974576,0.5422858,0.02766318,0.01009752],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05424945,"threshold_uncertainty_score":0.1814825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729919156646367,"score_gpt":0.3726181183376496,"score_spread":0.355318926771186,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}