{"id":"W2756281840","doi":"10.1016/j.ddmod.2017.08.002","title":"Genome wide conditional mouse knockout resources","year":2016,"lang":"en","type":"article","venue":"Drug Discovery Today Disease Models","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Children's Hospital; Toronto Centre for Phenogenomics; SickKids Foundation; Mount Sinai Hospital; University of Manitoba","funders":"National Human Genome Research Institute; National Institutes of Health; Centre National de la Recherche Scientifique; Université de Strasbourg; Institut National de la Santé et de la Recherche Médicale; Agence Nationale de la Recherche; European Commission; Genome British Columbia; Genome Canada","keywords":"Conditional gene knockout; Genome; Computational biology; Genetics; Biology; Knockout mouse; Computer science; Gene; Phenotype","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":[0.0000760144,0.0002058278,0.0001303287,0.00004682776,0.0000782851,0.00005224165,0.0002182006,0.00005500578,0.00005430676],"category_scores_gemma":[0.00004153909,0.0001574966,0.0001570036,0.00004059245,0.00008542812,0.00002675609,0.0001377786,0.00004233295,0.00003194918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002145394,"about_ca_system_score_gemma":0.00007130304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005112523,"about_ca_topic_score_gemma":0.000005163944,"domain_scores_codex":[0.9988318,0.00003264157,0.0001893953,0.0004345491,0.000201114,0.0003105047],"domain_scores_gemma":[0.9992008,0.00002215052,0.00004157061,0.0004437579,0.00004125764,0.0002504992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005428343,0.0002874225,0.004715962,0.00008018282,0.0002266566,0.00003276212,0.0002044493,0.1393378,0.8433822,0.00232568,0.008068918,0.0007951692],"study_design_scores_gemma":[0.007756774,0.0003320449,0.1074593,0.0003466753,0.0004041318,0.00002491746,0.0004192193,0.006545297,0.6171588,0.03902447,0.2160221,0.004506254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8668987,0.001653831,0.1294832,0.0004133996,0.00007472436,0.0001415335,0.0006800666,0.00003169076,0.0006227817],"genre_scores_gemma":[0.9862811,0.0002728447,0.00008731983,0.0003106904,0.0002783021,0.00004994898,0.0003598133,0.00004013089,0.01231985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2262233,"threshold_uncertainty_score":0.6422525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006559302728496631,"score_gpt":0.2419344639549065,"score_spread":0.2353751612264099,"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."}}