{"id":"W2944658903","doi":"10.3389/fgene.2019.00452","title":"Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":130,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Diamantina Institute, University of Queensland; Université Laval; L'Oreal USA","keywords":"Feature selection; Biomarker discovery; Computer science; Machine learning; Profiling (computer programming); Artificial intelligence; Context (archaeology); Data mining; Software; Biomarker; Categorical variable; Feature (linguistics); Omics; Bioinformatics; Proteomics; Biology","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":[0.00400687,0.001925705,0.001531481,0.003938193,0.0009986107,0.001430862,0.001325673,0.0007027991,0.004087474],"category_scores_gemma":[0.009242575,0.0005657416,0.001663708,0.003713531,0.0004105075,0.001301112,0.001573053,0.001252491,0.002639612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000923397,"about_ca_system_score_gemma":0.00169791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004073723,"about_ca_topic_score_gemma":0.005410674,"domain_scores_codex":[0.9981751,0.000580576,0.0002069198,0.0004230835,0.0004967247,0.0001176525],"domain_scores_gemma":[0.9971755,0.001734125,0.0001906277,0.0003992545,0.0004036411,0.00009680857],"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":[0.0009230537,0.0004544066,0.009521535,0.0005577881,0.0004507917,0.0004849716,0.0002057883,0.04121679,0.04203731,0.00426752,0.07631197,0.8235681],"study_design_scores_gemma":[0.000239431,0.0001542709,0.0108062,0.00005408775,0.0001052018,0.000337974,0.00008611161,0.9060587,0.0405879,0.02072882,0.02075654,0.00008473227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02739949,0.0006204114,0.9007598,0.0005264926,0.00009814517,0.0002543549,0.009810475,0.05973597,0.0007949111],"genre_scores_gemma":[0.1532916,0.0002810345,0.8141592,0.0002630645,0.00007851876,0.0008933984,0.02830597,0.001470563,0.001256683],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004087474,"threshold_uncertainty_score":0.02119064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053283672566128,"score_gpt":0.2558195365858338,"score_spread":0.2452866998601726,"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."}}