{"id":"W2761940999","doi":"10.1093/bioinformatics/btx609","title":"A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto","funders":"National Research Foundation of Korea; Canadian Institutes of Health Research; National Research Foundation; Neurosciences Research Foundation","keywords":"Haplotype; Single-nucleotide polymorphism; Computational biology; Genetics; Graph; Interval (graph theory); Genome; Biology; Block (permutation group theory); Computer science; Gene; Combinatorics; Mathematics; Theoretical computer science; Genotype","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":[0.001066078,0.0007831204,0.0008906586,0.002222609,0.0006094937,0.0008428852,0.001741293,0.0006760435,0.004502345],"category_scores_gemma":[0.003189012,0.000640596,0.001255972,0.001487651,0.0003806112,0.00116906,0.001176778,0.001094074,0.001757804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005341549,"about_ca_system_score_gemma":0.00108908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006995023,"about_ca_topic_score_gemma":0.01216686,"domain_scores_codex":[0.9993144,0.0001195049,0.00003978733,0.0002119521,0.00026453,0.00004979491],"domain_scores_gemma":[0.998585,0.0006358663,0.000157181,0.0002320601,0.0002825555,0.0001074082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004017759,0.0002081708,0.0133691,0.0004050221,0.0004395374,0.0003874627,0.0004367092,0.1466693,0.02898362,0.0131929,0.02056685,0.7749395],"study_design_scores_gemma":[0.00005186689,0.00002931189,0.001388996,0.00001292896,0.00002670808,0.000148826,0.00002830842,0.9837438,0.003414738,0.006763906,0.004368745,0.00002187176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006360272,0.00009450627,0.9880391,0.00005485583,0.00002787604,0.00007976637,0.0004600793,0.004543166,0.0003403905],"genre_scores_gemma":[0.04306633,0.00006527899,0.9521847,0.00006124533,0.00003864463,0.000154915,0.002271155,0.0006943409,0.00146339],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006995023,"threshold_uncertainty_score":0.01506186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05327049569628053,"score_gpt":0.309840703049359,"score_spread":0.2565702073530784,"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."}}