{"id":"W2746617822","doi":"10.1145/3107411.3108202","title":"Outlier Genes as Biomarkers of Breast Cancer Survivability in Time-Series Data","year":2017,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Outlier; Breast cancer; Hierarchical clustering; Survivability; Computer science; Computational biology; Data mining; Gene; Biology; Pattern recognition (psychology); Bioinformatics; Cancer; Artificial intelligence; 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":[0.001983426,0.0004367401,0.0005674631,0.002018543,0.0003621912,0.0008525622,0.0004628552,0.0006182848,0.0003107598],"category_scores_gemma":[0.006474814,0.0001185913,0.0004131093,0.002115914,0.000505182,0.0007482996,0.0004032411,0.0005126882,0.0001256403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005077992,"about_ca_system_score_gemma":0.0003999973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001759317,"about_ca_topic_score_gemma":0.00141195,"domain_scores_codex":[0.9992267,0.0002398639,0.00008096753,0.0001873875,0.0001940035,0.00007101183],"domain_scores_gemma":[0.996732,0.001650057,0.0008059312,0.0002897621,0.0003934252,0.0001287842],"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":[0.001878991,0.0004174913,0.601661,0.0003713368,0.000437988,0.0006760673,0.0007499236,0.1230936,0.07984363,0.004428552,0.000972301,0.1854692],"study_design_scores_gemma":[0.00002450992,0.0003994296,0.3147576,0.00003702325,0.0001435245,0.0004611955,0.0005232063,0.6547997,0.02059105,0.006957418,0.001233624,0.00007168764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8735021,0.0006234727,0.1244605,0.0001368105,0.00003190142,0.00005918983,0.0004799235,0.0003507676,0.0003553521],"genre_scores_gemma":[0.9798252,0.0001083366,0.01932349,0.0000106955,0.00001694452,0.00003266723,0.0005515204,0.0000187666,0.0001123088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002018543,"threshold_uncertainty_score":0.01048946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02943623514721954,"score_gpt":0.3241125264163612,"score_spread":0.2946762912691416,"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."}}