{"id":"W4307457738","doi":"10.3389/fcell.2022.1021785","title":"OMIXCARE: OMICS technologies solved about 33% of the patients with heterogeneous rare neuro-developmental disorders and negative exome sequencing results and identified 13% additional candidate variants","year":2022,"lang":"en","type":"article","venue":"Frontiers in Cell and Developmental Biology","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; St Joseph's Health Care; London Health Sciences Centre","funders":"Agence Nationale de la Recherche; Université de Bourgogne; European Commission","keywords":"Exome sequencing; DNA sequencing; Exome; Biology; Genetics; DNA methylation; Genomics; Genome; Disease; Omics; Epigenetics; Kabuki syndrome; Whole genome sequencing; Bioinformatics; Computational biology; Medicine; Gene; Mutation; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00409701,0.001446519,0.001141985,0.002944226,0.0007678768,0.001629972,0.0009139781,0.001477917,0.006514437],"category_scores_gemma":[0.005863743,0.0003504338,0.001157079,0.002672062,0.0003280087,0.0009501269,0.002369585,0.001055257,0.002408639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007414935,"about_ca_system_score_gemma":0.001505373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001936938,"about_ca_topic_score_gemma":0.003026268,"domain_scores_codex":[0.9984103,0.0003848695,0.0001649177,0.0004892474,0.0003670848,0.0001834897],"domain_scores_gemma":[0.9978638,0.0008832884,0.0004334212,0.0002854434,0.0003507101,0.0001832732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002380695,0.0004348842,0.2728718,0.004441533,0.00259805,0.00262443,0.001158101,0.001570658,0.1844191,0.005862897,0.07538583,0.4462521],"study_design_scores_gemma":[0.0006556811,0.0008984227,0.4739481,0.001906913,0.002233162,0.008236975,0.0005673689,0.008532357,0.1251513,0.00704034,0.3705967,0.0002327851],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5342585,0.07617174,0.129862,0.01381539,0.001401457,0.001739125,0.2015396,0.01075585,0.03045624],"genre_scores_gemma":[0.5950719,0.02359692,0.170052,0.009753409,0.001539798,0.002254431,0.1856627,0.002057883,0.01001097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006514437,"threshold_uncertainty_score":0.02179295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00349946249459717,"score_gpt":0.1664080280651134,"score_spread":0.1629085655705163,"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."}}