{"id":"W2793123174","doi":"10.1101/271957","title":"SMuRF: a novel tool to identify genomic regions enriched for somatic point mutations","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; Princess Margaret Cancer Centre; University Health Network","funders":"Prostate Cancer Canada; Government of Canada; Ontario Institute for Cancer Research; Movember Foundation; Princess Margaret Cancer Foundation; Canadian Institutes of Health Research; Genome Canada; Stand Up To Cancer; Entertainment Industry Foundation; Government of Ontario; American Association for Cancer Research","keywords":"Somatic cell; Biology; Genetics; Point mutation; Gene; Single-nucleotide polymorphism; Computational biology; Mutation; Genotype","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":[0.004413821,0.004714401,0.003362011,0.01339634,0.00211471,0.003259787,0.004935842,0.002343827,0.09639782],"category_scores_gemma":[0.01976949,0.002121588,0.00489065,0.006617735,0.001148901,0.002963561,0.004421794,0.002540912,0.03724266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099105,"about_ca_system_score_gemma":0.002200839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004893679,"about_ca_topic_score_gemma":0.006131074,"domain_scores_codex":[0.9969105,0.0005260134,0.0002420563,0.001288098,0.0007291956,0.0003041682],"domain_scores_gemma":[0.992408,0.00541293,0.0007813624,0.0005710907,0.0005229924,0.0003034906],"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":[0.001754675,0.0001703242,0.02747635,0.005594532,0.001610412,0.002803581,0.001802322,0.006938401,0.01651539,0.006471385,0.7894682,0.1393943],"study_design_scores_gemma":[0.002171336,0.0005013289,0.04895333,0.001570546,0.001469988,0.00817321,0.000935125,0.1713696,0.06034319,0.04629868,0.6571968,0.00101697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.02167806,0.002768546,0.1604513,0.000883867,0.0007089758,0.0004406151,0.270199,0.5369926,0.005877067],"genre_scores_gemma":[0.1299795,0.001354407,0.4088408,0.001630947,0.0005669516,0.002799356,0.2829629,0.1650945,0.006770548],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.09639782,"threshold_uncertainty_score":0.3224828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01390775873429712,"score_gpt":0.2490129612661828,"score_spread":0.2351052025318857,"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."}}