{"id":"W2127251093","doi":"10.1142/s0219720012500217","title":"A FLEXIBLE NONPARAMETRIC APPROACH TO FIND CANDIDATE GENES ASSOCIATED WITH DISEASE IN MICROARRAY EXPERIMENTS","year":2012,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; McMaster University; Public Health Ontario; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Candidate gene; Gene; Function (biology); Computational biology; Nonparametric statistics; Biology; Microarray; Genetics; Microarray analysis techniques; DNA microarray; Gene prediction; Gene expression; Mathematics; Statistics; Genome","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":[0.0002076011,0.0000872834,0.0001279182,0.0001949485,0.0000367369,0.00001720493,0.00008327604,0.0000583778,0.000002742681],"category_scores_gemma":[0.0000441008,0.00006367988,0.00002897019,0.0001838392,0.00003550333,0.0000134246,0.00003439099,0.00005526937,0.000001215513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002119064,"about_ca_system_score_gemma":0.0000961821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000216205,"about_ca_topic_score_gemma":3.602518e-7,"domain_scores_codex":[0.999353,0.0000364199,0.0002943928,0.00007270822,0.00009316867,0.0001503066],"domain_scores_gemma":[0.9994356,0.00001659481,0.0002193593,0.00006416091,0.0001042153,0.0001600854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002711322,0.00252077,0.6403627,0.0001552566,0.000648649,0.000003403614,0.003649631,0.06676977,0.2263713,0.002778879,0.006495614,0.04753271],"study_design_scores_gemma":[0.005165639,0.001642965,0.9349807,0.0001354988,0.00007134454,0.0001048774,0.001133151,0.01473883,0.02376216,0.0005192279,0.01701855,0.0007270871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9483554,0.00126482,0.04978267,0.00008206097,0.00008447435,0.0001068003,0.00001520177,0.000001899882,0.0003066313],"genre_scores_gemma":[0.9873577,0.00006759025,0.01204932,0.0003173522,0.00005652697,0.0000075327,0.00009405145,0.000004967394,0.00004494802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.294618,"threshold_uncertainty_score":0.259679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01719228709739987,"score_gpt":0.2766244980998253,"score_spread":0.2594322110024254,"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."}}