{"id":"W2259890056","doi":"10.1038/gim.2015.156","title":"VisCap: inference and visualization of germ-line copy-number variants from targeted clinical sequencing data","year":2015,"lang":"en","type":"article","venue":"Genetics in Medicine","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Princess Margaret Cancer Foundation","keywords":"Copy-number variation; Copy number analysis; Visualization; Computational biology; DNA sequencing; Workflow; Whole genome sequencing; Computer science; Genetics; Biology; Genome; Bioinformatics; Data mining; Gene; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005610115,0.0001145189,0.000242877,0.00003305665,0.00001578701,0.000006140683,0.0002594301,0.0001323677,0.00003030407],"category_scores_gemma":[0.000949378,0.00009938965,0.00001435077,0.00009044436,0.0001715832,0.000003437385,0.0003204956,0.00006544529,0.000002377856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008774089,"about_ca_system_score_gemma":0.0002178344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002317278,"about_ca_topic_score_gemma":0.0001662377,"domain_scores_codex":[0.9987644,0.0000971435,0.0004753088,0.0003736918,0.0001544571,0.0001350321],"domain_scores_gemma":[0.9989663,0.00004408554,0.0001513133,0.0005487793,0.0001464032,0.0001430887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002029804,0.0001509595,0.8537454,0.00004601583,0.0000974948,0.00003233433,0.000393156,0.000344462,0.1344433,0.0001115268,0.004544372,0.005888017],"study_design_scores_gemma":[0.01765169,0.00327674,0.806597,0.0007112749,0.0006113307,0.00007415838,0.002505657,0.06055515,0.06804159,0.009551886,0.02880935,0.001614208],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911218,0.00352373,0.004523744,0.00007333886,0.0002786045,0.000114075,0.0001203187,0.000004130905,0.0002402905],"genre_scores_gemma":[0.9925596,0.00258328,0.002522625,0.0002606663,0.0003828986,0.000001980922,0.001645216,0.00001474071,0.00002901572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06640169,"threshold_uncertainty_score":0.4052992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1274529409672691,"score_gpt":0.423545746144167,"score_spread":0.2960928051768978,"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."}}