{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000420168,0.0002267825,0.0002283304,0.00009477611,0.0002477055,0.00007186565,0.0002031362,0.0007574447,0.00001350205],"category_scores_gemma":[0.0000890291,0.000211818,0.00008604029,0.0002080099,0.0002431149,0.000005035165,0.00009636459,0.0004994366,9.65808e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001739528,"about_ca_system_score_gemma":0.00004577541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004202438,"about_ca_topic_score_gemma":0.00004896673,"domain_scores_codex":[0.9985768,0.000289177,0.0002379479,0.0005321001,0.0001073458,0.0002565843],"domain_scores_gemma":[0.9993446,0.00001819637,0.00007420056,0.0003780688,0.0001086054,0.00007628765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002437604,0.00006401216,0.001335869,0.0000484097,0.00006496556,0.000003136068,0.00001411718,8.205649e-7,0.884603,0.00249811,0.0003578575,0.1109853],"study_design_scores_gemma":[0.000722905,0.0003213449,0.002486932,0.00004227648,0.0000779519,0.00001476305,0.0000244807,0.0000329972,0.7235093,0.001240171,0.2712398,0.0002870581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9104467,0.005028326,0.0594392,0.0001898351,0.0008024324,0.000669611,0.00002070462,0.0001163057,0.02328687],"genre_scores_gemma":[0.686533,0.0000964673,0.3111841,0.0003670756,0.0001779627,0.00004080629,0.0001805325,0.00002653337,0.001393556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.270882,"threshold_uncertainty_score":0.8637689,"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."}}