{"id":"W2517179206","doi":"10.1093/bioinformatics/btw536","title":"SiNVICT: ultra-sensitive detection of single nucleotide variants and indels in circulating tumour DNA","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Indel; Computational biology; DNA sequencing; Liquid biopsy; Biology; Deep sequencing; False positive paradox; DNA; Genome; Cancer; Single-nucleotide polymorphism; Genetics; Computer science; Gene; Genotype; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001933305,0.0008131532,0.0008819338,0.001131697,0.0005633641,0.001087012,0.001503945,0.001152501,0.001976696],"category_scores_gemma":[0.004269988,0.0005037448,0.0008169417,0.0009277496,0.000621106,0.0006689493,0.000896791,0.001027584,0.0007257934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006343034,"about_ca_system_score_gemma":0.001565889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002965805,"about_ca_topic_score_gemma":0.005675223,"domain_scores_codex":[0.9988214,0.000256989,0.00005265717,0.0002831112,0.0005309657,0.00005493064],"domain_scores_gemma":[0.9971385,0.001775687,0.0003632829,0.0002041913,0.0004021381,0.000116283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001373714,0.0004012093,0.03790772,0.002775328,0.0007324009,0.001610915,0.0004927391,0.2856182,0.1791756,0.02013329,0.02684859,0.4429303],"study_design_scores_gemma":[0.00007638204,0.0001916702,0.003104576,0.0000545435,0.00006279763,0.000849124,0.00003772566,0.9306077,0.05110828,0.004911462,0.00889306,0.0001026437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08217669,0.002001585,0.8899105,0.000459096,0.0005018085,0.0002761865,0.00244457,0.0163473,0.005882341],"genre_scores_gemma":[0.2557017,0.0006758485,0.734622,0.0004534484,0.0001100156,0.0004690455,0.003710286,0.001046298,0.003211278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002965805,"threshold_uncertainty_score":0.0102244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00948069767126719,"score_gpt":0.2085639938520451,"score_spread":0.1990832961807779,"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."}}