{"id":"W4406833231","doi":"10.1038/s42256-024-00942-3","title":"Moving towards genome-wide data integration for patient stratification with Integrate Any Omics","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; Princess Margaret Cancer Centre; Canadian Institute for Advanced Research; Vector Institute; University of Toronto; University Health Network","funders":"","keywords":"Omics; Risk stratification; Genome; Computational biology; Stratification (seeds); Biology; Medicine; Bioinformatics; Genetics; Internal medicine; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002056859,0.0001996916,0.0001476616,0.00007405318,0.0001010322,0.00008874208,0.0005410827,0.0002366932,0.000005628791],"category_scores_gemma":[0.0006602663,0.0001635397,0.00004698064,0.0001763287,0.00005782485,0.00001079845,0.0002005201,0.0002927312,0.000001543497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005713069,"about_ca_system_score_gemma":0.0003006763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009835492,"about_ca_topic_score_gemma":0.001103617,"domain_scores_codex":[0.998835,0.00002333176,0.0002825959,0.0005568592,0.0001062877,0.0001959792],"domain_scores_gemma":[0.998672,0.00006701239,0.0001235209,0.0008052511,0.0002814982,0.00005069512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001442346,0.0003298953,0.003724262,0.000172642,0.0003161845,0.00000486354,0.0003445714,0.005515616,0.1903796,0.01842389,0.006470707,0.7728754],"study_design_scores_gemma":[0.0003661998,0.0007968937,0.002375975,0.0001083487,0.0001167075,0.000008365726,0.0004532588,0.02879704,0.8312163,0.003977772,0.1312082,0.0005749686],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06671008,0.004542514,0.9244837,0.000966851,0.0004706363,0.0007064166,0.0006757583,0.00002179515,0.001422217],"genre_scores_gemma":[0.9801357,0.0007622595,0.01375187,0.001209362,0.0001286943,0.00005588551,0.003760923,0.0000211773,0.0001741751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9134256,"threshold_uncertainty_score":0.6668957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013743343988668,"score_gpt":0.2859616925736326,"score_spread":0.2758242591337459,"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."}}