{"id":"W2514228312","doi":"10.12688/f1000research.9417.3","title":"Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning","year":2017,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cytodiagnostics (Canada); University of Windsor; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Breast cancer; Biology; Medicine; Oncology; Internal medicine; Cancer","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":[0.0008381619,0.000331922,0.0003367735,0.0006832744,0.0003233597,0.0004733595,0.000313585,0.0003125875,0.0004311646],"category_scores_gemma":[0.00178345,0.0001376187,0.0002580975,0.0007614166,0.0001426953,0.0001967103,0.0006155402,0.0003366311,0.0001700189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005920925,"about_ca_system_score_gemma":0.0006621649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01136742,"about_ca_topic_score_gemma":0.0172183,"domain_scores_codex":[0.9997012,0.0001217007,0.00002107462,0.00007133294,0.00004745621,0.00003716478],"domain_scores_gemma":[0.9992614,0.000125477,0.0002666176,0.0001406567,0.0001035731,0.0001023335],"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":[0.0004673189,0.00006804444,0.9880998,0.0000112077,0.00007141796,0.00005486375,0.00006425266,0.0008870023,0.001016606,0.00006871363,0.000679162,0.008511809],"study_design_scores_gemma":[0.00005532841,0.0003409005,0.985906,0.000009634407,0.0001101687,0.0003740656,0.0001644776,0.009853147,0.001009797,0.0001359073,0.002028503,0.00001207892],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976754,0.0001274515,0.0005643503,0.00006542714,0.000004330596,0.00003119978,0.001159271,0.00001639018,0.00035634],"genre_scores_gemma":[0.993605,0.00006972372,0.001965668,0.00005079175,0.00001018068,0.00004801891,0.003936604,0.000009024585,0.0003051728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01136742,"threshold_uncertainty_score":0.0226025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03095765592080526,"score_gpt":0.3360658616825897,"score_spread":0.3051082057617845,"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."}}