{"id":"W2948477377","doi":"10.1038/s41591-019-0457-8","title":"Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts","year":2019,"lang":"en","type":"article","venue":"Nature Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":355,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Newborn Screening Ontario; Children's Hospital of Eastern Ontario","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; National Institutes of Health","keywords":"Exome sequencing; Transcriptome; RNA-Seq; Disease; Biology; Exome; Gene; Molecular diagnostics; Genetics; RNA; Phenotype; False discovery rate; Computational biology; Bioinformatics; Medicine; Gene expression; 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":[0.0001829277,0.0001034001,0.0001786098,0.00004973622,0.00003426395,0.000005016728,0.00007638444,0.0001334293,0.00002287686],"category_scores_gemma":[0.00006362739,0.00008333939,0.00004512564,0.00005419683,0.00005339829,0.000003288184,0.00001578924,0.00008150236,5.752634e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006323513,"about_ca_system_score_gemma":0.00007039661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008537515,"about_ca_topic_score_gemma":0.000003479832,"domain_scores_codex":[0.9992723,0.00002813275,0.0002042776,0.0002379463,0.0001287634,0.0001285791],"domain_scores_gemma":[0.9994636,0.000008181828,0.00009831494,0.0002223536,0.0001081522,0.00009939259],"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":[0.00008470787,0.00002673514,0.03003123,0.0001096842,0.00005936464,0.000009681463,0.00003965124,0.00002489117,0.9693487,0.00007862968,0.00004568125,0.0001410553],"study_design_scores_gemma":[0.01763135,0.0008917564,0.1720314,0.0005280308,0.002250355,0.0002031183,0.001042755,0.005717352,0.7806612,0.000508677,0.01753659,0.0009974358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9644674,0.03450346,0.0002998144,0.0001136028,0.0002425791,0.0002381811,0.00009500197,0.000005069527,0.00003487216],"genre_scores_gemma":[0.9987963,0.0003570564,0.00003619731,0.0003760871,0.0001970623,0.000003326824,0.000120048,0.00001335258,0.0001005427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1886875,"threshold_uncertainty_score":0.3398482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004510163611937029,"score_gpt":0.2419988206626428,"score_spread":0.2374886570507057,"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."}}