{"id":"W3034005935","doi":"10.1101/2020.06.06.137356","title":"Genome-Wide Sequencing as a First-Tier Screening Test for Short Tandem Repeat Expansions","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Neurodegenerative Diseases","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Children's & Women's Health Centre of British Columbia; University of British Columbia","funders":"Canadian Institutes of Health Research; BC Children's Hospital; Michael Smith Health Research BC; Compute Canada; Genome British Columbia; Rare Disease Foundation; Children's Hospital Foundation","keywords":"Genetics; Exome sequencing; Locus (genetics); Biology; Trinucleotide repeat expansion; Microsatellite; Whole genome sequencing; Genome; Exome; Concordance; Genotype; Computational biology; Allele; Mutation; Gene","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":[0.002049677,0.0007488629,0.0006120285,0.001089337,0.0002619742,0.0006907333,0.0004165241,0.0007023752,0.001310258],"category_scores_gemma":[0.004336718,0.0002739473,0.0005306036,0.000535013,0.0002121538,0.0002735195,0.0004454799,0.0005598374,0.0004694428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002858513,"about_ca_system_score_gemma":0.0003646099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001991552,"about_ca_topic_score_gemma":0.003642301,"domain_scores_codex":[0.998696,0.0005228664,0.000078161,0.0003201879,0.0002756226,0.0001070944],"domain_scores_gemma":[0.9981504,0.001220381,0.000162555,0.00008421515,0.0002076138,0.0001747865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00239631,0.0004463025,0.8113516,0.0001570864,0.0003889287,0.00161582,0.0002476254,0.0177221,0.107276,0.0003019338,0.001756742,0.05633962],"study_design_scores_gemma":[0.0001490241,0.002106099,0.5831438,0.00007180784,0.0003665912,0.003825423,0.0002583393,0.3374408,0.06920263,0.0008802768,0.002478806,0.00007636662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9844624,0.0002842283,0.01279827,0.0001257805,0.00001591242,0.00008809098,0.001286652,0.0003484744,0.0005900417],"genre_scores_gemma":[0.9818366,0.00007356421,0.01641101,0.00009973961,0.000008355069,0.00004962308,0.001225921,0.00002868189,0.0002664854],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002049677,"threshold_uncertainty_score":0.01083982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04972415272767924,"score_gpt":0.2568814418605059,"score_spread":0.2071572891328267,"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."}}