{"id":"W2952191365","doi":"10.1093/bioinformatics/btz485","title":"SIGN: similarity identification in gene expression","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Montreal Clinical Research Institute; Institute of Cancer Research; Ontario Institute for Cancer Research; Institut universitaire de cardiologie et de pneumologie de Québec; Université de Montréal; Université Laval; Princess Margaret Cancer Centre; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Institute for Cancer Research; Canadian Institutes of Health Research; Government of Ontario; Cancer Research Society","keywords":"Similarity (geometry); Identification (biology); Expression (computer science); Computer science; Computational biology; Breast cancer; Sign (mathematics); Artificial intelligence; Machine learning; Data mining; Bioinformatics; Biology; Cancer; Genetics; Mathematics","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":[0.003615556,0.001192877,0.001220057,0.002974418,0.0004818476,0.001702796,0.001356814,0.0007939488,0.007122643],"category_scores_gemma":[0.02053775,0.000406706,0.00107922,0.003605631,0.001302769,0.001546349,0.002036249,0.001676273,0.004959434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006148079,"about_ca_system_score_gemma":0.001318594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008102746,"about_ca_topic_score_gemma":0.0009876267,"domain_scores_codex":[0.9959385,0.001267799,0.0002624354,0.001206597,0.001149377,0.0001753622],"domain_scores_gemma":[0.99214,0.004417303,0.001275731,0.0009217532,0.0008614995,0.0003837933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002638247,0.0005884574,0.08320823,0.003500149,0.0007927968,0.0008554668,0.0006992945,0.0889902,0.08319111,0.05636089,0.1403833,0.538792],"study_design_scores_gemma":[0.000309057,0.0009169876,0.03574321,0.0002263979,0.000216576,0.001863387,0.0001767456,0.7173285,0.05076164,0.1280761,0.06413734,0.0002439981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04999691,0.0009221113,0.8823532,0.0008962503,0.0002568449,0.0004433385,0.02870373,0.03128991,0.005137624],"genre_scores_gemma":[0.3942353,0.000764068,0.5451695,0.0006349524,0.0003454559,0.001567713,0.04890705,0.004104107,0.00427181],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007122643,"threshold_uncertainty_score":0.02382761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009305137900203311,"score_gpt":0.2299095297661017,"score_spread":0.2206043918658984,"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."}}