{"id":"W2954775438","doi":"","title":"The Autism Speaks Mssng Whole Genome Sequencing Precision Medicine Resource","year":2019,"lang":"en","type":"article","venue":"INSAR 2019 Annual Meeting","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Autism; Precision medicine; Whole genome sequencing; Resource (disambiguation); Biology; Genomics; Computational biology; Genetics; Genome; Computer science; Medicine; Gene; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01008295,0.001299834,0.001813568,0.004306195,0.001430046,0.003714984,0.002708943,0.002955717,0.1017709],"category_scores_gemma":[0.02934194,0.001029354,0.001050638,0.002729156,0.0006362443,0.001956078,0.007129141,0.002708728,0.0722198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009302602,"about_ca_system_score_gemma":0.006276305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008727818,"about_ca_topic_score_gemma":0.01924094,"domain_scores_codex":[0.9961846,0.0009026771,0.000308446,0.0005853354,0.001539408,0.0004795097],"domain_scores_gemma":[0.9805409,0.006212318,0.001326007,0.003362643,0.003197401,0.005360683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002765375,0.00002146873,0.0007419048,0.0001630821,0.00005233982,0.000104599,0.00003371236,0.0001209453,0.001382957,0.001294927,0.9590051,0.0368024],"study_design_scores_gemma":[0.0003451696,0.00005285454,0.003825206,0.0002506045,0.00009717275,0.0002956148,0.00004802177,0.0009359349,0.001410461,0.008138833,0.9845408,0.0000594328],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005644983,0.0069354,0.04021459,0.04094543,0.01030893,0.0006254013,0.7077574,0.05128271,0.1362852],"genre_scores_gemma":[0.02871452,0.004450513,0.08756528,0.03495671,0.007677965,0.002351709,0.7360227,0.01551431,0.08274639],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1017709,"threshold_uncertainty_score":0.3404576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006881058070895339,"score_gpt":0.2334311604090292,"score_spread":0.2265501023381339,"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."}}