{"id":"W2968512494","doi":"10.1109/tcbb.2019.2934407","title":"Deletion Detection Method Using the Distribution of Insert Size and a Precise Alignment Strategy","year":2019,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Breakpoint; Insert (composites); Computer science; Structural variation; Algorithm; Computational biology; Genome; Genetics; Biology; Gene; Chromosome","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001702143,0.0001045214,0.000112392,0.00002439997,0.0001450444,0.00001123694,0.00005952403,0.000105403,0.000003346944],"category_scores_gemma":[0.00001266014,0.00007768505,0.00003726155,0.00005651459,0.00009401693,0.000003124843,0.00001008329,0.00006387027,0.000001017407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001060404,"about_ca_system_score_gemma":0.00003040017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001856844,"about_ca_topic_score_gemma":0.000009009695,"domain_scores_codex":[0.9994365,0.00005444678,0.0002288119,0.0001262017,0.00005750817,0.00009653071],"domain_scores_gemma":[0.9995443,0.0001284511,0.0001015934,0.0001190948,0.00008111966,0.00002550189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005584828,0.0001819086,0.002199451,0.0001665981,0.000543854,2.016351e-7,0.0005974247,0.2809789,0.5336664,0.0006587363,0.00002008569,0.1804279],"study_design_scores_gemma":[0.004387979,0.005229034,0.05416111,0.00008934452,0.0003952486,0.0002782269,0.002376113,0.5711458,0.3404284,0.01708441,0.003381819,0.001042541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6296159,0.00009909674,0.3698802,0.00004808345,0.00009013438,0.000156769,0.00009195832,0.00000185984,0.00001604152],"genre_scores_gemma":[0.9849865,0.0002057535,0.01463568,0.00007875184,0.0000201236,0.000008147716,0.00004849484,0.000004066083,0.00001241483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3553707,"threshold_uncertainty_score":0.3167904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497111749995502,"score_gpt":0.2777145677557809,"score_spread":0.2627434502558259,"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."}}