{"id":"W1670192389","doi":"10.1016/j.molonc.2015.07.006","title":"Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning","year":2015,"lang":"en","type":"article","venue":"Molecular Oncology","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cytodiagnostics (Canada); London Health Sciences Centre; Western University","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Compute Canada","keywords":"Gemcitabine; Paclitaxel; Breast cancer; Cancer research; Biology; Cancer; Oncology; Medicine; Genetics","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":[0.0003559242,0.0001891407,0.0002932154,0.0007714336,0.0001102011,0.0003041464,0.0001189926,0.000263832,0.000754923],"category_scores_gemma":[0.001166681,0.0001267004,0.0002536246,0.0005277256,0.0001951688,0.0001321464,0.0001646333,0.0002426952,0.0001831038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224948,"about_ca_system_score_gemma":0.0001365783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006810699,"about_ca_topic_score_gemma":0.0008441938,"domain_scores_codex":[0.999739,0.00008437533,0.00001589726,0.00006358515,0.0000626546,0.0000345569],"domain_scores_gemma":[0.9995127,0.0002519757,0.000130911,0.00004966544,0.00003383344,0.00002089262],"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":[0.001323196,0.0001677323,0.7360207,0.00008231107,0.0002531141,0.0004463554,0.0001578721,0.01433973,0.1666209,0.0003828196,0.0005807374,0.07962456],"study_design_scores_gemma":[0.00002124239,0.0003039119,0.9267086,0.00001094218,0.0001098886,0.001105411,0.00008312301,0.05370798,0.01609651,0.0009116411,0.0009225802,0.00001824226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964791,0.0003428705,0.002442274,0.00006208741,0.000003481926,0.00000945697,0.0002204532,0.00004794523,0.0003924776],"genre_scores_gemma":[0.9979703,0.00007701654,0.00118776,0.00002275977,0.000004281503,0.00001312632,0.0005520715,0.000004780098,0.0001679608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007714336,"threshold_uncertainty_score":0.002525449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007996193839503967,"score_gpt":0.2631648589612554,"score_spread":0.2551686651217514,"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."}}