{"id":"W2908677338","doi":"10.1093/bioinformatics/btz012","title":"A web application and service for imputing and visualizing missense variant effect maps","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"National Human Genome Research Institute; Canadian Institutes of Health Research; National Institutes of Health; Canada Research Chairs","keywords":"Missense mutation; Imputation (statistics); Computer science; Pipeline (software); Software; Source code; Data mining; Web service; Missing data; Open source; Exome; Computational biology; Biology; Mutation; World Wide Web; Genetics; Machine learning; Exome sequencing; Gene; Programming language","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":[0.0001410728,0.0000860556,0.00008868778,0.00001986445,0.00005716352,0.00003795064,0.00004467207,0.00006803158,5.10411e-7],"category_scores_gemma":[0.00002384397,0.00007696443,0.00002468233,0.00002893078,0.00001075268,0.000004099622,0.0000710586,0.00001997655,0.000005141235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003637373,"about_ca_system_score_gemma":0.00002475953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004552072,"about_ca_topic_score_gemma":0.000003045658,"domain_scores_codex":[0.9995959,0.000007872713,0.0001354568,0.0001090667,0.00003590175,0.0001158075],"domain_scores_gemma":[0.9996754,0.00002252244,0.00006943555,0.0001366737,0.00003924972,0.0000567159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004521236,0.00007641053,0.005106491,0.004672301,0.0002235543,0.000004698573,0.001031582,0.0001088142,0.917208,0.002530423,0.001480448,0.06710511],"study_design_scores_gemma":[0.01013665,0.002409771,0.007034289,0.0002503434,0.000349455,0.001048686,0.00219797,0.7429595,0.06631044,0.0007362261,0.1647591,0.001807596],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961949,0.0003498654,0.002598013,0.00006371804,0.00004498046,0.0004648668,0.0000474521,0.000008997325,0.0002271986],"genre_scores_gemma":[0.9944741,0.0000589194,0.004669084,0.0005021039,0.00005482527,0.00001692093,0.0001864107,0.00001136588,0.00002631952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8508976,"threshold_uncertainty_score":0.3138518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004101546701540773,"score_gpt":0.2333109660772478,"score_spread":0.229209419375707,"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."}}