{"id":"W4280527126","doi":"10.1096/fasebj.2022.36.s1.r3971","title":"miRNA‐Based Cancer Classifier from TCGA Expression Profiles","year":2022,"lang":"en","type":"article","venue":"The FASEB Journal","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"microRNA; Computational biology; Biology; Ovarian cancer; Cluster analysis; Biomarker discovery; Prostate cancer; Colorectal cancer; Feature selection; Cancer; Bioinformatics; Computer science; Artificial intelligence; Genetics; Gene; Proteomics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008018772,0.0006358599,0.0007322116,0.002297154,0.000371687,0.001037093,0.0006670252,0.0006558985,0.002353466],"category_scores_gemma":[0.004079504,0.0001452839,0.0007247018,0.0009215609,0.0001698991,0.0003091576,0.0003810205,0.0005651984,0.001311468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008481317,"about_ca_system_score_gemma":0.001158248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005741848,"about_ca_topic_score_gemma":0.004848137,"domain_scores_codex":[0.9993438,0.00007630265,0.00008724895,0.0002143879,0.0001850795,0.00009313432],"domain_scores_gemma":[0.9988618,0.0004387413,0.0001184443,0.0001016501,0.0004247423,0.00005459229],"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":[0.001642567,0.0007746794,0.2387718,0.0006138852,0.0005353272,0.0005921459,0.0002584705,0.1218168,0.07566871,0.002045285,0.02278719,0.5344932],"study_design_scores_gemma":[0.00010126,0.0006214342,0.05559758,0.00009220075,0.0002895433,0.0006928978,0.0001524382,0.8673329,0.05832446,0.002778171,0.01394766,0.00006938496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.744164,0.001591211,0.2166156,0.0008461676,0.0002346924,0.001654785,0.01889351,0.00706281,0.008937055],"genre_scores_gemma":[0.8564349,0.0002302575,0.1236272,0.0002409249,0.00007174619,0.001098062,0.01565158,0.0001474411,0.002497762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005741848,"threshold_uncertainty_score":0.01141685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01693490387090402,"score_gpt":0.2584792466358228,"score_spread":0.2415443427649188,"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."}}