{"id":"W2944510853","doi":"10.1093/bioinformatics/btz308","title":"gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks","year":2019,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Hospital for Sick Children; Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"Canadian Statistical Sciences Institute; Canadian Institutes of Health Research; National Research Foundation of Korea; Neurosciences Research Foundation","keywords":"Bioconductor; Visualization; Computer science; Cluster analysis; Genome; Tag SNP; dbSNP; Linkage disequilibrium; Data mining; Single-nucleotide polymorphism; R package; Haplotype; Computational biology; SNP genotyping; Reference genome; DNA sequencing; Biology; Genetics; Gene; Artificial intelligence; Computational science","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":[0.001765709,0.001996043,0.001362489,0.002977897,0.0008794863,0.002570509,0.002659095,0.001002813,0.07585818],"category_scores_gemma":[0.007409275,0.001359327,0.002173146,0.003124844,0.0003933969,0.00157595,0.003217977,0.0020232,0.02042659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003984487,"about_ca_system_score_gemma":0.00113719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003094187,"about_ca_topic_score_gemma":0.003493293,"domain_scores_codex":[0.9990441,0.0002807633,0.00007100096,0.0002447251,0.0002762584,0.00008311436],"domain_scores_gemma":[0.9982261,0.0009569512,0.0001983018,0.0002734762,0.0002405813,0.0001045603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008968865,0.0001228827,0.007409128,0.002340878,0.0008383964,0.00107052,0.001606246,0.01516746,0.02297718,0.01278415,0.7183377,0.2164487],"study_design_scores_gemma":[0.001112587,0.0002966995,0.02522458,0.0007079784,0.0005166926,0.002744122,0.0005118346,0.4165875,0.04993832,0.0692345,0.4325314,0.0005938011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01244784,0.0006525217,0.573946,0.000916734,0.0003425013,0.0003178064,0.08433652,0.32168,0.005360034],"genre_scores_gemma":[0.09700157,0.001026829,0.7402766,0.000512901,0.0002343938,0.001969271,0.08591663,0.06743119,0.005630674],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.07585818,"threshold_uncertainty_score":0.2537709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03210594169097992,"score_gpt":0.3011335103091326,"score_spread":0.2690275686181527,"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."}}