{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005877014,0.002425421,0.001648653,0.002726482,0.001031816,0.002437803,0.00497933,0.002934507,0.09181378],"category_scores_gemma":[0.02574891,0.001714518,0.001866931,0.002508938,0.000751908,0.002801156,0.004136295,0.003120461,0.05449223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008947461,"about_ca_system_score_gemma":0.002275918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001146987,"about_ca_topic_score_gemma":0.002451535,"domain_scores_codex":[0.9965951,0.0007665817,0.000370903,0.0009741942,0.001084975,0.000208154],"domain_scores_gemma":[0.9919381,0.004640446,0.0005540458,0.001469601,0.001035071,0.0003628105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001338504,0.00009912044,0.003195915,0.003326357,0.0003273089,0.001148681,0.0004928515,0.003654462,0.02084211,0.01261023,0.7759935,0.1769709],"study_design_scores_gemma":[0.0005855076,0.0002695582,0.003472422,0.0005490472,0.0001727052,0.001466142,0.0001427927,0.04008477,0.06597247,0.02856397,0.858513,0.0002075531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.002849476,0.001094933,0.3357154,0.001024822,0.001336477,0.0003200601,0.07500693,0.5764764,0.006175477],"genre_scores_gemma":[0.03377194,0.001227131,0.6546022,0.002854061,0.0006656526,0.001909614,0.1798848,0.1075294,0.01755534],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.09181378,"threshold_uncertainty_score":0.3071477,"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."}}