{"id":"W2899165605","doi":"10.1101/460915","title":"Fast and accurate shared segment detection and relatedness estimation in un-phased genetic data using TRUFFLE","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Lunenfeld-Tanenbaum Research Institute; Public Health Ontario; University of Toronto; Mount Sinai Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Truffle; Identity by descent; Computer science; Identification (biology); Data mining; Computational biology; Biology; Haplotype; Genetics; Gene; 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":[0.003784169,0.0008337541,0.0008956612,0.002577183,0.0006449497,0.00148788,0.001410267,0.0009788036,0.002734014],"category_scores_gemma":[0.01145773,0.0006655463,0.0006836656,0.001818732,0.0003840752,0.001427142,0.001232792,0.001487881,0.00200963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002906276,"about_ca_system_score_gemma":0.0006811516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531681,"about_ca_topic_score_gemma":0.003139903,"domain_scores_codex":[0.9984986,0.0006005933,0.00009516042,0.0003691421,0.0003536346,0.000082934],"domain_scores_gemma":[0.9937508,0.003552779,0.0007129529,0.001109383,0.0006759366,0.0001981916],"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":[0.001169571,0.000521123,0.05489454,0.001231633,0.0008384028,0.001110607,0.001273753,0.07984343,0.2342922,0.01371367,0.05254693,0.5585641],"study_design_scores_gemma":[0.000227417,0.0001989107,0.01722508,0.0001333956,0.00009184649,0.0006635121,0.0001704944,0.8719836,0.07200474,0.01782961,0.01923781,0.0002335154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09605248,0.000437304,0.875605,0.0002859715,0.00006724228,0.00008166277,0.003649875,0.02290739,0.0009129896],"genre_scores_gemma":[0.1902888,0.0001656218,0.8012075,0.00019161,0.00004026887,0.0002308337,0.005843507,0.001165222,0.0008665477],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003784169,"threshold_uncertainty_score":0.0200128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02643244296077737,"score_gpt":0.2680728138136313,"score_spread":0.241640370852854,"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."}}