{"id":"W2949868745","doi":"10.1101/678250","title":"Human and mouse essentiality screens as a resource for disease gene discovery","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; SickKids Foundation; Toronto Centre for Phenogenomics; Hospital for Sick Children","funders":"National Center for Research Resources; Government of Canada; National Institutes of Health; Genome Canada; Ontario Genomics; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute","keywords":"Biology; Gene; Genetics; Disease; OMIM : Online Mendelian Inheritance in Man; Lethal allele; Computational biology; Organism; Mendelian inheritance; Model organism; Human disease; Function (biology); Phenotype; Medicine","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":[0.004361974,0.0008859514,0.001001218,0.003360812,0.0005492263,0.0009911748,0.001110424,0.0006405453,0.006713055],"category_scores_gemma":[0.002570366,0.0004721138,0.0007997589,0.001389257,0.0003962708,0.0004374747,0.002377319,0.0007613011,0.002401246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002593196,"about_ca_system_score_gemma":0.0007513934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006831983,"about_ca_topic_score_gemma":0.001340647,"domain_scores_codex":[0.9982046,0.000622778,0.0001942971,0.0003300277,0.0004951355,0.0001531708],"domain_scores_gemma":[0.9973499,0.0009186749,0.0003519672,0.0009460785,0.0001716056,0.0002618626],"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":[0.002577842,0.0002975162,0.01988474,0.0008863999,0.0006792923,0.002240772,0.0004227727,0.003190504,0.8039548,0.008509581,0.03366789,0.1236878],"study_design_scores_gemma":[0.0009157622,0.001258138,0.06010608,0.0003328831,0.0009421798,0.008090129,0.000369062,0.01657383,0.6094437,0.0161177,0.2856272,0.00022336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3869095,0.003212931,0.4352908,0.002025477,0.0004249285,0.001184961,0.1228123,0.02851879,0.01962024],"genre_scores_gemma":[0.477874,0.001609751,0.3344655,0.001180097,0.000207149,0.001135753,0.1721431,0.003996123,0.007388479],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006713055,"threshold_uncertainty_score":0.02306861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009820512789171142,"score_gpt":0.2300138503452799,"score_spread":0.2201933375561088,"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."}}