{"id":"W2890693027","doi":"10.1093/bioinformatics/bty812","title":"PRESM: personalized reference editor for somatic mutation discovery in cancer genomics","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Canada Foundation for Innovation; Arkansas Children’s Hospital Research Institute","keywords":"Somatic cell; Genomics; Computational biology; Mutation; Cancer genetics; Personalized medicine; Germline mutation; Cancer; Computer science; Genetics; Biology; Genome; Gene","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":[0.0001220064,0.0001301961,0.0001343068,0.00004787823,0.00006818793,0.00006670238,0.0001550146,0.0001242252,0.00001211593],"category_scores_gemma":[0.0001467941,0.000126469,0.00005199706,0.00006134558,0.00009533491,0.00001787563,0.00006568996,0.00004513068,0.00001197349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007334341,"about_ca_system_score_gemma":0.0002658349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005203056,"about_ca_topic_score_gemma":0.0003669346,"domain_scores_codex":[0.9992132,0.000008817937,0.0003133242,0.0001434327,0.00009084638,0.0002304375],"domain_scores_gemma":[0.9994411,0.00003577305,0.0001378167,0.0002190681,0.0001150645,0.00005114996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004305043,0.001145768,0.01772959,0.003510121,0.0008187845,0.000008777134,0.01818404,0.004072952,0.3458467,0.01057099,0.4782859,0.1155214],"study_design_scores_gemma":[0.00559257,0.001541048,0.004006078,0.0001729326,0.0001135293,0.00001481611,0.001686905,0.06205887,0.1065565,0.001165297,0.8159849,0.00110657],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9564778,0.0005365051,0.03725556,0.0001959975,0.00238321,0.0008562248,0.0005938812,0.00001663764,0.001684189],"genre_scores_gemma":[0.9523063,0.001855326,0.03365956,0.001327911,0.008076827,0.0004364006,0.001023383,0.00006371667,0.001250604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.337699,"threshold_uncertainty_score":0.5157257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682069656366527,"score_gpt":0.2798510192886188,"score_spread":0.2630303227249535,"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."}}