{"id":"W2010646309","doi":"10.1371/journal.pone.0123946","title":"Developing a Prognostic Micro-RNA Signature for Human Cervical Carcinoma","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; Ontario Institute for Cancer Research","keywords":"Cervical cancer; Concordance; Oncology; Medicine; microRNA; Gene expression profiling; Carcinoma; RNA; Cancer; Disease; Bioinformatics; Internal medicine; Biology; Pathology; Gene expression; Gene; 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":[0.0006641184,0.0002943627,0.0004678953,0.0006456455,0.0002291392,0.000542546,0.0002792831,0.0003544649,0.000815218],"category_scores_gemma":[0.001539342,0.0001371225,0.0002564393,0.0004691435,0.0001922099,0.0004092841,0.0004641046,0.0003863766,0.0005269189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002723711,"about_ca_system_score_gemma":0.0005713342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003468086,"about_ca_topic_score_gemma":0.0007734895,"domain_scores_codex":[0.9996464,0.00006593297,0.00003768211,0.00008688589,0.0001204326,0.0000425895],"domain_scores_gemma":[0.9995754,0.0001317975,0.00009972035,0.00004731761,0.0001111851,0.00003453639],"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":[0.0005824311,0.00007650747,0.05622196,0.0002957387,0.00007803751,0.0003381321,0.0001574891,0.004064162,0.850627,0.0007007761,0.001003955,0.08585376],"study_design_scores_gemma":[0.00005328077,0.001178843,0.1386658,0.0001291028,0.0002727031,0.002770938,0.000319084,0.05481713,0.7809281,0.002852812,0.01792942,0.00008281592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236923,0.002843991,0.06567159,0.0005951842,0.00009245259,0.0003167424,0.002674786,0.0005490356,0.003563772],"genre_scores_gemma":[0.9249569,0.0007943203,0.06958376,0.0002968828,0.00003903801,0.000192045,0.003050607,0.00004830947,0.001038085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.000815218,"threshold_uncertainty_score":0.003512204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05832774482917714,"score_gpt":0.2660143372637583,"score_spread":0.2076865924345812,"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."}}