{"id":"W2618209500","doi":"10.1186/s12864-017-3671-0","title":"Comprehensive whole genome sequence analyses yields novel genetic and structural insights for Intellectual Disability","year":2017,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's & Women's Health Centre of British Columbia; Child and Family Research Institute; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Canadian Institutes of Health Research; Terveyden ja hyvinvoinnin laitos; Trinity College Dublin; Michael Smith Health Research BC","keywords":"Indel; Biology; Genetics; Whole genome sequencing; Intellectual disability; Structural variation; Genome; Exome sequencing; Copy-number variation; Computational biology; Gene; Sequence assembly; INDEL Mutation; Reference genome; DNA sequencing; DNA microarray; Mutation; Single-nucleotide polymorphism; Genotype; Transcriptome","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":[0.0005749456,0.0006740672,0.0003763644,0.001674343,0.0003327426,0.0005946945,0.0003043256,0.0005176587,0.002043891],"category_scores_gemma":[0.001143294,0.0001835002,0.0007254722,0.001055698,0.0003241081,0.0002562184,0.0006072819,0.0005592126,0.0005050636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003035977,"about_ca_system_score_gemma":0.0003391826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009965854,"about_ca_topic_score_gemma":0.002496135,"domain_scores_codex":[0.999669,0.00003596183,0.00003182285,0.0001361728,0.0000816095,0.00004543643],"domain_scores_gemma":[0.9993305,0.0002524505,0.0001441334,0.0001159512,0.00009691837,0.00005998347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004559509,0.0001410914,0.1962481,0.0004486222,0.0006689093,0.002538739,0.0003734107,0.003767583,0.7304955,0.0007887225,0.001346097,0.06272735],"study_design_scores_gemma":[0.00004340324,0.0004155064,0.816264,0.0001141785,0.0007738987,0.00765815,0.0003521718,0.009976829,0.1459044,0.002484632,0.01596161,0.00005121545],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9683371,0.0009916053,0.01964306,0.0001713137,0.00002648356,0.00005409509,0.008656196,0.0002991433,0.001821139],"genre_scores_gemma":[0.9579236,0.0009489694,0.0275761,0.0001869947,0.00003425857,0.00005494788,0.0122836,0.0001463512,0.0008451432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002043891,"threshold_uncertainty_score":0.006837547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07916574211285611,"score_gpt":0.327530364372378,"score_spread":0.2483646222595219,"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."}}