{"id":"W2891541687","doi":"10.1101/408492","title":"Identification of rare-disease genes in diverse undiagnosed cases using whole blood transcriptome sequencing and large control cohorts","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Common Fund; NIH Office of the Director; Ontario Genomics Institute; Canadian Institutes of Health Research; National Institute of Standards and Technology; Ontario Genomics; Genome Canada; Office of Strategic Coordination; National Institutes of Health; National Science Foundation","keywords":"Exome sequencing; Disease; Biology; Genetics; Rare disease; RNA-Seq; Disease gene identification; Microcephaly; Bioinformatics; Transcriptome; Gene; Computational biology; Medicine; Phenotype; Gene expression; Internal 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":[0.00293283,0.0005737656,0.0005113434,0.001240963,0.0009807509,0.001118438,0.0005916632,0.0007723696,0.00205089],"category_scores_gemma":[0.004867715,0.0003043555,0.0005880601,0.0006621277,0.0009001392,0.000234787,0.0008490498,0.000640054,0.0003566725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003778735,"about_ca_system_score_gemma":0.0003190193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003423999,"about_ca_topic_score_gemma":0.005466645,"domain_scores_codex":[0.9975528,0.0004859419,0.0001810558,0.001293201,0.0003534699,0.0001334819],"domain_scores_gemma":[0.9978962,0.000735238,0.0003257535,0.0006024652,0.000272313,0.0001680206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002457258,0.0005463638,0.6438565,0.0003637424,0.00203662,0.005545535,0.002564567,0.004076536,0.2847756,0.002894646,0.005865024,0.04501755],"study_design_scores_gemma":[0.0004920712,0.0008678021,0.9146023,0.0001668313,0.00109969,0.008720988,0.0008020887,0.01177769,0.04044699,0.003187441,0.01772096,0.0001150629],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721034,0.0006727962,0.02234349,0.000173808,0.00005503079,0.0001587415,0.003166607,0.0002094783,0.001116646],"genre_scores_gemma":[0.9861044,0.0002104752,0.00810235,0.0002673259,0.00004406221,0.0002283256,0.004262321,0.0001354018,0.0006451996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003423999,"threshold_uncertainty_score":0.0155105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01666172503411402,"score_gpt":0.2421785309271557,"score_spread":0.2255168058930417,"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."}}