{"id":"W2926399644","doi":"10.3389/fgene.2019.00282","title":"BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"Helse Sør-Øst RHF; Norges Forskningsråd; Kreftforeningen","keywords":"Computational biology; Gene; Biology; Genetics; Genome; Carcinogenesis; DNA binding site; DNA sequencing; Promoter; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009501084,0.0001484394,0.0001806767,0.000104359,0.00004041002,0.00002567276,0.0001037964,0.0001447136,0.00001722794],"category_scores_gemma":[0.00007148817,0.0001739083,0.0000686271,0.0001249477,0.00002591159,0.000003848692,0.0000689736,0.00009425676,0.000001997498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001164743,"about_ca_system_score_gemma":0.0003037911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001037408,"about_ca_topic_score_gemma":0.0004623873,"domain_scores_codex":[0.9989147,0.00001559294,0.0002920978,0.0003672634,0.0001111668,0.0002991836],"domain_scores_gemma":[0.9995078,0.00002581464,0.00009329586,0.000233441,0.00005720925,0.00008247801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001770403,0.00008523074,0.859433,0.00005342143,0.00005576464,0.000003000426,0.0004352242,0.04360205,0.04779148,0.00002270957,0.02972075,0.01862032],"study_design_scores_gemma":[0.01156588,0.002210342,0.4268661,0.0004317621,0.0001880204,0.00001388593,0.002479985,0.07884661,0.3049596,0.002632074,0.1677925,0.00201326],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.903163,0.002489608,0.09069816,0.0001020447,0.002391457,0.00074361,0.0001281758,0.000006121329,0.0002778877],"genre_scores_gemma":[0.9825857,0.0009444421,0.01504328,0.0002827489,0.0003815187,0.000139249,0.0002536419,0.00004145275,0.0003279931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4325669,"threshold_uncertainty_score":0.7091776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00802060231604924,"score_gpt":0.2349289016489737,"score_spread":0.2269082993329244,"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."}}