{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006593543,0.0001710518,0.0001737156,0.00005677345,0.0002610463,0.00001491792,0.000149227,0.00008504979,0.000008913764],"category_scores_gemma":[0.00003531756,0.0001341382,0.0000227643,0.00009617199,0.0003436033,0.000007282413,0.0005765473,0.0001074467,1.149983e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005574629,"about_ca_system_score_gemma":0.0001573356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003874466,"about_ca_topic_score_gemma":0.00007662706,"domain_scores_codex":[0.999001,0.00006000823,0.0002310294,0.0004227704,0.00007639918,0.0002087678],"domain_scores_gemma":[0.9996921,0.00002183635,0.000131173,0.00009122154,0.0000242203,0.00003946741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002367816,0.000202185,0.9342683,0.00009897893,0.0003182172,0.00003079467,0.001915012,0.0001055141,0.04535925,0.00001542236,0.00446353,0.01085497],"study_design_scores_gemma":[0.01215568,0.001151775,0.8909477,0.00008867578,0.00007247263,0.0001853837,0.02450502,0.0001470174,0.056419,0.001628909,0.01128487,0.001413529],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951101,0.000752373,0.00002185773,0.00003860071,0.0001763075,0.0002818042,0.003527194,0.000007177662,0.00008460037],"genre_scores_gemma":[0.9953605,0.0005812091,0.002608108,0.00008674376,0.000005161704,0.0000530793,0.001245691,0.00001241621,0.00004707942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04332063,"threshold_uncertainty_score":0.5469998,"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."}}