{"id":"W3028935303","doi":"10.1101/2020.05.29.124495","title":"GeneBreaker - Variant simulation to improve the diagnosis of Mendelian rare genetic diseases","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"University of British Columbia; Genome British Columbia; Michael Smith Health Research BC; Compute Canada; Canadian Institutes of Health Research; Genome Canada","keywords":"Mendelian inheritance; Prioritization; Computational biology; Personalized medicine; Genomics; Disease; Precision medicine; Genetic testing; Population; Biology; Genetics; Gene; Genome; Bioinformatics; Medicine; Engineering","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":[0.00278163,0.0006859436,0.0006672423,0.0008433717,0.0004568739,0.000946194,0.001510186,0.001541986,0.006124272],"category_scores_gemma":[0.008972119,0.0003993754,0.0009857406,0.0004826647,0.0006684401,0.0006139158,0.00111942,0.001098473,0.0007426466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009498816,"about_ca_system_score_gemma":0.001262875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006949088,"about_ca_topic_score_gemma":0.005064624,"domain_scores_codex":[0.9993333,0.0003330837,0.00003207082,0.0001034872,0.000143946,0.00005412701],"domain_scores_gemma":[0.9952666,0.003907328,0.0001486824,0.0002617406,0.0002532552,0.0001624406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002393405,0.00007490734,0.006035794,0.00008154708,0.00008044306,0.00031044,0.00008584092,0.9600398,0.002090593,0.0125466,0.004304538,0.0141102],"study_design_scores_gemma":[0.00004264457,0.0000249447,0.0002194925,0.000008786611,0.000009626801,0.00005194982,0.000009225702,0.9901952,0.001193008,0.006484766,0.001752227,0.000008106696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.164704,0.0005109655,0.7995164,0.001937444,0.0004842012,0.0002398189,0.003391475,0.01918124,0.01003439],"genre_scores_gemma":[0.6825375,0.0002439154,0.3087757,0.0005433021,0.00008360111,0.0002537791,0.003101324,0.001493522,0.002967437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006949088,"threshold_uncertainty_score":0.02048767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01091913025657776,"score_gpt":0.2247798652782289,"score_spread":0.2138607350216511,"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."}}