{"id":"W3216448540","doi":"10.1038/s41586-021-04278-5","title":"Multi-omic machine learning predictor of breast cancer therapy response","year":2021,"lang":"en","type":"article","venue":"Nature","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":642,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Trinity College, University of Cambridge; Medical Research Council; National Institute for Health and Care Research; University of Cambridge; Cancer Research UK; Cancer Research UK Cambridge Institute, University of Cambridge; NIHR Cambridge Biomedical Research Centre; Wellcome Trust","keywords":"Breast cancer; Medicine; Transcriptome; Disease; Oncology; Cancer; Digital pathology; Machine learning; Internal medicine; Bioinformatics; Pathology; Computer science; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003478464,0.0008417483,0.001283483,0.002283033,0.0003382176,0.00164332,0.0004294831,0.0006378025,0.001480364],"category_scores_gemma":[0.006060023,0.0001655222,0.0009236499,0.001745143,0.0003585377,0.0006417426,0.0008392443,0.0008833648,0.0004358837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005830934,"about_ca_system_score_gemma":0.0004012059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001202204,"about_ca_topic_score_gemma":0.001331952,"domain_scores_codex":[0.9985362,0.0006583314,0.0001184311,0.0003191621,0.0002524999,0.0001154222],"domain_scores_gemma":[0.9947843,0.003150277,0.001017778,0.0004426547,0.00043373,0.0001712158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007349229,0.0004244519,0.9008309,0.0001865442,0.001525704,0.0001027026,0.00005184889,0.03379611,0.008285873,0.0003140275,0.001140055,0.05260689],"study_design_scores_gemma":[0.00003459301,0.0005978754,0.518322,0.00009443201,0.0004797986,0.0002778369,0.000141976,0.4675934,0.005670627,0.00508943,0.001649112,0.00004897929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509612,0.002917417,0.03750965,0.001306996,0.00008208169,0.00007394525,0.00550325,0.000329177,0.001316242],"genre_scores_gemma":[0.9919972,0.0001804581,0.005811277,0.0001124661,0.00004058688,0.00002535588,0.001668941,0.0000122142,0.0001514586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003478464,"threshold_uncertainty_score":0.01839614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005727007160292818,"score_gpt":0.262274046546969,"score_spread":0.2565470393866761,"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."}}