{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003446102,0.0001422946,0.00009570649,0.00002874311,0.0003381957,0.0000747939,0.0004625002,0.0001416379,0.0000271336],"category_scores_gemma":[0.0001034183,0.00009847676,0.00008832043,0.0001130919,0.0004640953,0.0000114002,0.0002484492,0.00009985496,0.000013677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002736449,"about_ca_system_score_gemma":0.0002269233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000386561,"about_ca_topic_score_gemma":0.0005604099,"domain_scores_codex":[0.9989367,0.00003194297,0.0004265737,0.0001667337,0.0002354376,0.0002026635],"domain_scores_gemma":[0.9986115,0.00003879434,0.0003665549,0.0004983456,0.0004383936,0.00004648874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009061879,0.0001607212,0.3518598,0.0001102353,0.0008314067,7.213967e-7,0.001303459,0.002181148,0.4761252,0.0132893,0.1337432,0.01948854],"study_design_scores_gemma":[0.001175783,0.0001639312,0.2499496,0.00002297955,0.00004996162,0.00002593841,0.0005496367,0.01854502,0.1183917,0.001207527,0.6095273,0.0003906738],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820085,0.0002742576,0.01210729,0.00298178,0.001090045,0.0003417599,0.0001022237,0.00001163903,0.001082518],"genre_scores_gemma":[0.9926724,0.0001569985,0.0006090829,0.003734564,0.0004273253,0.00002212729,0.000518782,0.00001705889,0.00184168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.475784,"threshold_uncertainty_score":0.4015766,"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."}}