{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003094435,0.0001299205,0.0001277312,0.00006128808,0.00003775908,0.00004528531,0.0002115792,0.0001922604,0.00003555233],"category_scores_gemma":[0.00002141729,0.0001210354,0.00005710273,0.00009837294,0.00002524502,0.00001766484,0.000125296,0.0001045362,0.000205473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002008003,"about_ca_system_score_gemma":0.00004439185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003221209,"about_ca_topic_score_gemma":0.00000571553,"domain_scores_codex":[0.9990273,0.00001630791,0.0004675557,0.0001284603,0.0001327601,0.0002275918],"domain_scores_gemma":[0.9992887,0.000008041313,0.0001603788,0.0004434915,0.00004197425,0.00005745811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002035393,0.0002263623,0.02731996,0.0004394155,0.00005699366,0.000002338641,0.001546045,0.005128799,0.9138415,0.0008719685,0.01900497,0.03135813],"study_design_scores_gemma":[0.003801343,0.0005012863,0.02931149,0.0001572162,0.00002672472,0.00004148199,0.001330916,0.1295525,0.7607591,0.001392031,0.07166692,0.00145896],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9672756,0.0001973581,0.02331006,0.00007317709,0.000444965,0.0005163787,0.00002702571,0.00002142641,0.008133968],"genre_scores_gemma":[0.9889026,0.00009874575,0.009644132,0.000336893,0.00008435069,0.00001133049,0.0002996093,0.00001256123,0.000609759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1530824,"threshold_uncertainty_score":0.4935681,"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."}}