{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002929147,0.0001659429,0.0002088395,0.0000753155,0.0002207174,0.00007057866,0.0005070732,0.00002842581,0.00002413355],"category_scores_gemma":[0.00007804014,0.0001247129,0.0001755005,0.0001231716,0.0003609401,0.00000439215,0.0001660467,0.00009855774,0.00002473407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002525011,"about_ca_system_score_gemma":0.0001196913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006038058,"about_ca_topic_score_gemma":0.000008607415,"domain_scores_codex":[0.998695,0.0001133821,0.0004615968,0.0002169893,0.0002455721,0.0002675236],"domain_scores_gemma":[0.9984741,0.000014408,0.0004365919,0.0004856104,0.0003331397,0.0002561564],"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":[0.0006229992,0.0002191578,0.004355579,0.000006864106,0.0001953977,0.00004260283,0.0002054589,0.0003342249,0.9746252,0.00001526584,0.006280664,0.01309654],"study_design_scores_gemma":[0.001756243,0.01949402,0.8527505,0.0001511755,0.0008674551,0.0005529865,0.001133501,0.0002412907,0.07407127,0.0017105,0.04628639,0.0009846624],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972388,0.0004734134,0.00124968,0.0002483531,0.0004740391,0.0001420421,0.00006790507,0.000006199688,0.00009958622],"genre_scores_gemma":[0.9958594,0.0001828627,0.000834728,0.0008420292,0.002099969,0.000002829195,0.00001260187,0.00003709549,0.0001284511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.900554,"threshold_uncertainty_score":0.5085643,"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."}}