{"id":"W4402396437","doi":"10.3389/fgene.2024.1336891","title":"BLESS: bagged logistic regression for biomarker identification","year":2024,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Saskatchewan","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Michael Smith Health Research BC; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Natural Sciences and Engineering Research Council of Canada; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Logistic regression; Identification (biology); Biomarker; Regression; Statistics; Computer science; Computational biology; Biology; Mathematics; Genetics","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":[0.007091035,0.002666096,0.0023084,0.003124853,0.0008217303,0.002120745,0.003548708,0.00241515,0.01300516],"category_scores_gemma":[0.02588544,0.001057059,0.002381578,0.00281388,0.0005796261,0.00224388,0.002902214,0.004150057,0.01469747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005435704,"about_ca_system_score_gemma":0.001845343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004781604,"about_ca_topic_score_gemma":0.006108927,"domain_scores_codex":[0.9960084,0.002176288,0.0002007239,0.0006414092,0.0007631364,0.0002099958],"domain_scores_gemma":[0.9926918,0.004425646,0.0005649122,0.001153919,0.0008930383,0.0002707148],"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":[0.001696394,0.0004880869,0.0142869,0.0006180172,0.002184593,0.0006408376,0.0001470982,0.1636884,0.005125486,0.01724243,0.1327568,0.6611249],"study_design_scores_gemma":[0.00009413048,0.0001094307,0.001491993,0.00005857328,0.00007828289,0.000176113,0.00002673939,0.9501165,0.001512163,0.03262457,0.01363093,0.00008061426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002803329,0.0009049642,0.9708845,0.0005453878,0.0003120605,0.0001114824,0.002635258,0.02128156,0.0005214798],"genre_scores_gemma":[0.1423949,0.001216637,0.8200518,0.001393966,0.0006556651,0.001010237,0.01647926,0.004672643,0.01212496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01300516,"threshold_uncertainty_score":0.04350662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0324041833855108,"score_gpt":0.3203195147854756,"score_spread":0.2879153313999648,"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."}}