{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002593826,0.001814822,0.001403618,0.004693186,0.001251305,0.001142612,0.003704779,0.001514063,0.0410485],"category_scores_gemma":[0.001184591,0.001997839,0.001419342,0.002846772,0.0006042816,0.0007092329,0.00205413,0.002411669,0.03267424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006810779,"about_ca_system_score_gemma":0.001905903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001996199,"about_ca_topic_score_gemma":0.007447911,"domain_scores_codex":[0.9988251,0.0001488994,0.0001448374,0.0002172379,0.0005203726,0.0001435708],"domain_scores_gemma":[0.9985203,0.0002933529,0.0001905477,0.0005578377,0.0001709622,0.0002669873],"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":[0.001078613,0.0002263187,0.00122432,0.000993143,0.0002666739,0.0006735841,0.0001487698,0.001481212,0.8297187,0.009336106,0.1122396,0.04261293],"study_design_scores_gemma":[0.0007415072,0.0002207901,0.00651391,0.0002404499,0.0005004797,0.001776153,0.00004659575,0.002581274,0.3289591,0.004104424,0.6541305,0.000184798],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03435792,0.003187267,0.2471389,0.001198163,0.0006456735,0.001681577,0.5950871,0.07350624,0.0431973],"genre_scores_gemma":[0.04040104,0.003879935,0.1448429,0.001049167,0.0001167841,0.003051853,0.7619752,0.01367857,0.03100469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0410485,"threshold_uncertainty_score":0.1373209,"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."}}