{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199817,0.0007979886,0.001102171,0.001823747,0.0006601061,0.001373574,0.001232842,0.0009457803,0.001575332],"category_scores_gemma":[0.006837389,0.0005243701,0.001455903,0.001498057,0.0006991343,0.0008454411,0.0008571596,0.001119733,0.0002879531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007577474,"about_ca_system_score_gemma":0.001711963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004474854,"about_ca_topic_score_gemma":0.005268259,"domain_scores_codex":[0.9992726,0.0003372118,0.00004925827,0.0001132899,0.0001922674,0.00003528629],"domain_scores_gemma":[0.9962226,0.003172674,0.0001689426,0.0001365704,0.0002299375,0.00006925222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009000867,0.0001072963,0.002967645,0.0001546989,0.0002122125,0.0001595908,0.00003977177,0.9266231,0.001468757,0.01073899,0.00123615,0.05620172],"study_design_scores_gemma":[0.000006928492,0.000011432,0.0001779233,0.000004618783,0.000009694555,0.00001396859,0.000005016903,0.9947098,0.0001526672,0.004692042,0.0002124501,0.000003432696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01362105,0.0002983917,0.983893,0.0006160327,0.00003295481,0.00007252792,0.0002994862,0.000596308,0.0005702152],"genre_scores_gemma":[0.2878041,0.0005534829,0.7083683,0.000349674,0.00013638,0.0007818579,0.0008928876,0.00007451524,0.001038751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004474854,"threshold_uncertainty_score":0.01056749,"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."}}