{"id":"W2804919189","doi":"10.1007/s10592-018-1072-9","title":"The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation","year":2018,"lang":"en","type":"article","venue":"Conservation Genetics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Toronto Centre for Phenogenomics; Mount Sinai Hospital","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Human Genome Research Institute; Bundesministerium für Bildung und Forschung; National Institutes of Health; Government of Canada; National Research Foundation; INFRAFRONTIER; Genome Canada; Ontario Genomics; Ontario Genomics Institute","keywords":"Biology; Gene; Genetics; Phenotype; Gene knockout; Genome; Candidate gene; Computational biology; Loss function; Genotyping; Function (biology); Model organism; Bioinformatics; 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":[0.004198287,0.001382416,0.001244514,0.01231793,0.001190576,0.001512139,0.002162359,0.001089259,0.01048984],"category_scores_gemma":[0.005156631,0.0006948896,0.0008452494,0.01080209,0.0006955473,0.001351892,0.002189476,0.001267813,0.005207549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000828995,"about_ca_system_score_gemma":0.002788344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008820409,"about_ca_topic_score_gemma":0.01143467,"domain_scores_codex":[0.9984087,0.0003599812,0.000192296,0.000287612,0.000588474,0.00016296],"domain_scores_gemma":[0.9951381,0.0008423316,0.001121545,0.001196908,0.0009154496,0.0007856368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001281941,0.0001604057,0.0528287,0.003426032,0.0003626652,0.0008534202,0.001269695,0.00363125,0.1206125,0.03077468,0.3382095,0.4465892],"study_design_scores_gemma":[0.00009583395,0.000125755,0.08045688,0.0005920425,0.0002601613,0.001077622,0.0001490857,0.001795241,0.01296098,0.00709003,0.8952801,0.0001162434],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05760274,0.00999309,0.2403779,0.00209392,0.0005283264,0.001068972,0.6276562,0.02210597,0.03857291],"genre_scores_gemma":[0.03648211,0.004427821,0.2237958,0.0007164223,0.0001100064,0.001582291,0.7231768,0.003649606,0.006059116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01231793,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01947794909163687,"score_gpt":0.2198337099786287,"score_spread":0.2003557608869919,"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."}}