{"id":"W2885723697","doi":"10.1101/383703","title":"Accurate Reference-Free Somatic Variant-Calling by Integrating Genomic, Sequencing and Population Data","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto; Ontario Institute for Cancer Research","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Prostate Cancer Canada; Government of Ontario; Canadian Institutes of Health Research; Genome Canada; Ontario Institute for Cancer Research; Movember Foundation","keywords":"Somatic cell; Context (archaeology); Computational biology; Biology; Population; Computer science; Reference genome; Precision and recall; Genetics; DNA sequencing; Artificial intelligence; Gene; Medicine","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":[0.008255482,0.001431369,0.001410889,0.002946217,0.0006652295,0.002150201,0.001239706,0.001192031,0.003774621],"category_scores_gemma":[0.01453444,0.0009390639,0.001352511,0.002401269,0.0007154892,0.001182848,0.001880529,0.001245182,0.003739644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007054381,"about_ca_system_score_gemma":0.001313868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003036312,"about_ca_topic_score_gemma":0.006859158,"domain_scores_codex":[0.9958121,0.0009333138,0.0002465728,0.001598011,0.001234789,0.0001751066],"domain_scores_gemma":[0.9944587,0.002338135,0.000628201,0.001464988,0.0009292468,0.0001807348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001856165,0.0003366496,0.1269191,0.001689034,0.002685165,0.0007683713,0.0009618594,0.06126146,0.3172379,0.01036991,0.03695463,0.4389598],"study_design_scores_gemma":[0.0002143256,0.0004428701,0.0771189,0.0002414176,0.0006854978,0.001352677,0.0003076677,0.45023,0.3671443,0.03013969,0.07183753,0.0002850752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1484528,0.00140307,0.790141,0.000380971,0.000247397,0.0002003904,0.01435143,0.04026878,0.004554253],"genre_scores_gemma":[0.3383373,0.0005020351,0.6280595,0.0004147806,0.0001171422,0.000259077,0.02386683,0.005006768,0.003436531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008255482,"threshold_uncertainty_score":0.04365969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02950269031629831,"score_gpt":0.2476474531554361,"score_spread":0.2181447628391378,"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."}}