{"id":"W2891643925","doi":"10.1016/j.ajhg.2018.08.005","title":"ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants","year":2018,"lang":"en","type":"article","venue":"The American Journal of Human Genetics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":291,"is_retracted":false,"has_abstract":false,"ca_institutions":"Children's Hospital of Eastern Ontario; McGill University; University of Ottawa; McGill Genome Centre; McGill University Health Centre","funders":"Compute Canada; Genome Canada","keywords":"Nonsynonymous substitution; Exome; Missense mutation; Exome sequencing; Population; Allele frequency; Machine learning; 1000 Genomes Project; Computational biology; Computer science; Biology; Genetics; Artificial intelligence; Mutation; Allele; Genome; Single-nucleotide polymorphism; Genotype; Medicine; 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":[0.001183753,0.002129649,0.001321107,0.002540034,0.0006560236,0.00109901,0.001051964,0.001483899,0.0190889],"category_scores_gemma":[0.00451297,0.0007414974,0.001496394,0.00155282,0.000253612,0.0006187081,0.001027407,0.001067805,0.006536289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002572513,"about_ca_system_score_gemma":0.001003728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007765187,"about_ca_topic_score_gemma":0.002390762,"domain_scores_codex":[0.9994942,0.0001353723,0.00006575183,0.0001590349,0.00009477541,0.00005083152],"domain_scores_gemma":[0.9981167,0.001283299,0.0001876909,0.0001209734,0.0001600956,0.000131261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00685668,0.0007293662,0.141139,0.006090315,0.002714073,0.008969198,0.0004072404,0.01935662,0.03947463,0.005613165,0.5355978,0.2330519],"study_design_scores_gemma":[0.004444588,0.001428631,0.1097566,0.001701367,0.00362844,0.03015535,0.0004707353,0.3545783,0.05250444,0.03879739,0.4020004,0.0005337209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.2630417,0.01085775,0.2233518,0.002392859,0.001642701,0.001002981,0.3146453,0.167364,0.01570102],"genre_scores_gemma":[0.4740141,0.002534363,0.2455898,0.002030375,0.000546704,0.001122762,0.2611381,0.008067266,0.004956474],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0190889,"threshold_uncertainty_score":0.06385869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384740879249283,"score_gpt":0.284809035553464,"score_spread":0.2709616267609711,"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."}}