{"id":"W2674532415","doi":"10.1186/s13073-017-0446-9","title":"ISOWN: accurate somatic mutation identification in the absence of normal tissue controls","year":2017,"lang":"en","type":"article","venue":"Genome Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Ontario Ministry of Research, Innovation and Science; Ontario Institute for Cancer Research","keywords":"Somatic cell; Computational biology; Exome sequencing; Human genetics; Germline; Germline mutation; Biology; Identification (biology); Exome; Genome; DNA sequencing; Genomics; Genetics; Mutation; Bioinformatics; 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":[0.0003849753,0.00006732205,0.0001143783,0.00002760068,0.00008074553,0.00001977121,0.0003172094,0.0000417153,0.00001911266],"category_scores_gemma":[0.0004148044,0.00004883321,0.00001770646,0.0000296016,0.0001301669,0.000004633007,0.00003865213,0.00004039441,0.000007409391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008199773,"about_ca_system_score_gemma":0.00003333541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001541326,"about_ca_topic_score_gemma":0.0001665977,"domain_scores_codex":[0.9993905,0.0000252242,0.0002441792,0.0001312071,0.0001045121,0.0001043097],"domain_scores_gemma":[0.9991646,0.00003222789,0.0002390426,0.0004797052,0.00006148079,0.00002298965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003235658,0.00002454993,0.002200121,0.00003154348,0.00001204124,0.000006299609,0.0005936595,0.0001612703,0.9924383,0.0002258724,0.0002112987,0.00406273],"study_design_scores_gemma":[0.002790582,0.0006785226,0.883942,0.00006897977,0.00005173195,0.00003590764,0.0009006798,0.000494664,0.09411776,0.001267565,0.01542857,0.0002230526],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927611,0.001243266,0.002691538,0.001673922,0.0001878171,0.0002754838,0.00001746854,0.000001704839,0.001147649],"genre_scores_gemma":[0.9988428,0.0003683614,0.00004897209,0.0002258731,0.0002723019,0.00002367765,0.00008534989,0.000005614894,0.000127088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8983205,"threshold_uncertainty_score":0.199136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0144294146505435,"score_gpt":0.2955641733997002,"score_spread":0.2811347587491567,"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."}}