{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002228444,0.0001264109,0.0001016953,0.0001440736,0.00005150663,0.00006022218,0.0001772938,0.000171428,0.00000798305],"category_scores_gemma":[0.00005532501,0.0001156133,0.00007109295,0.0001909002,0.00005775885,0.000003621449,0.00003901305,0.00005893658,0.000005944511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000384393,"about_ca_system_score_gemma":0.00008780402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001335559,"about_ca_topic_score_gemma":0.000004079417,"domain_scores_codex":[0.9989774,0.00004572378,0.0002530458,0.0004287548,0.0001109005,0.0001841368],"domain_scores_gemma":[0.9994888,0.000008909477,0.0000531247,0.0003427145,0.00005655476,0.00004990766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000850944,0.00003487742,0.00236598,0.0001008808,0.00002854763,0.000001317467,0.00006319168,0.000144326,0.7120247,0.00008584826,0.2193067,0.06575845],"study_design_scores_gemma":[0.0005664346,0.00009807103,0.009116978,0.0001283061,0.00004015277,0.000003855594,0.0002562602,0.02207149,0.3110504,0.001813006,0.6545371,0.0003178641],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1568903,0.04007209,0.7949849,0.0007267925,0.005556785,0.0008503357,0.00007089797,0.00006288217,0.0007850199],"genre_scores_gemma":[0.9813154,0.001972798,0.01163534,0.00009029687,0.000239102,0.000184587,0.0003291636,0.00003667674,0.004196583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8244252,"threshold_uncertainty_score":0.4714573,"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."}}