{"id":"W4293680078","doi":"10.3390/diagnostics12081997","title":"A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data","year":2022,"lang":"en","type":"article","venue":"Diagnostics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Southeastern Ontario Academic Medical Organization; Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Biomarker discovery; Computer science; Computational biology; Feature (linguistics); Identification (biology); Omics; Data mining; Bioinformatics; Machine learning; Gene; Biology; Proteomics; 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":[0.0002375864,0.0000683253,0.00007692524,0.0000995243,0.00004894505,0.000007880808,0.0003198106,0.00003047352,0.00001263322],"category_scores_gemma":[0.0001664921,0.00007818161,0.00001803677,0.0002412072,0.00002523853,0.000002754936,0.000365988,0.00004990118,0.000002203958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002233927,"about_ca_system_score_gemma":0.0001047808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000411804,"about_ca_topic_score_gemma":0.000003519992,"domain_scores_codex":[0.9990343,0.00007855707,0.0002356039,0.0003262276,0.0002277088,0.00009753613],"domain_scores_gemma":[0.9993369,0.00002118805,0.00009315767,0.000449321,0.00005538073,0.00004406581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001534004,0.000559793,0.008613968,0.00002390639,0.00006913045,0.00000355815,0.00009417626,0.3158715,0.6360494,0.001607626,0.03476034,0.002193153],"study_design_scores_gemma":[0.005755147,0.0007227085,0.3199984,0.00005896312,0.0001384406,0.00003457911,0.001238314,0.1790775,0.4358785,0.002821934,0.05281626,0.001459175],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9639522,0.0004500885,0.03415094,0.0003553742,0.0001901656,0.0002926308,0.0004686424,0.000005703902,0.0001342724],"genre_scores_gemma":[0.9918693,0.00002204303,0.00253378,0.0003017556,0.00001942175,0.00008454955,0.005130704,0.00001057649,0.00002789631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3113845,"threshold_uncertainty_score":0.3188154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557660400358468,"score_gpt":0.2700504973351285,"score_spread":0.2544738933315439,"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."}}