{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01381666,0.0007306572,0.001293228,0.002504001,0.0008386081,0.001079602,0.001792187,0.001605833,0.001085611],"category_scores_gemma":[0.03718537,0.000405861,0.001352091,0.002181767,0.002114736,0.0009650522,0.001471148,0.001665878,0.0003463957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006570607,"about_ca_system_score_gemma":0.001110176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001065678,"about_ca_topic_score_gemma":0.001378909,"domain_scores_codex":[0.9911301,0.005866256,0.0003040152,0.001237281,0.001252438,0.0002099812],"domain_scores_gemma":[0.9754015,0.01873788,0.001658417,0.002764685,0.001161124,0.0002763596],"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":[0.001409108,0.0006746615,0.03847172,0.0004441211,0.0007369563,0.0008791552,0.0004621095,0.293155,0.0524172,0.02828565,0.003410247,0.5796542],"study_design_scores_gemma":[0.00007695986,0.0003415643,0.01215663,0.00001605459,0.00005268004,0.0003905069,0.00004307064,0.9637091,0.004423513,0.01754322,0.001178791,0.00006790356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02795578,0.0001125223,0.9707292,0.00009976443,0.00002115608,0.0001009352,0.00008691855,0.0006523553,0.0002413251],"genre_scores_gemma":[0.4290957,0.0000667357,0.5688172,0.000132864,0.00007075938,0.0006849737,0.0003725624,0.00008522181,0.000673961],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01381666,"threshold_uncertainty_score":0.07307035,"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."}}