{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00387861,0.001315915,0.0007513275,0.003754871,0.000473699,0.002001436,0.001531468,0.0008692279,0.01251932],"category_scores_gemma":[0.01532273,0.0008238888,0.0009840745,0.001495588,0.0004324371,0.001156795,0.002055019,0.0009422582,0.002913019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005862128,"about_ca_system_score_gemma":0.001112191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001971185,"about_ca_topic_score_gemma":0.002312779,"domain_scores_codex":[0.9984485,0.0003839984,0.0001578339,0.0004290103,0.00051849,0.00006219214],"domain_scores_gemma":[0.9935629,0.004286955,0.0006447559,0.0005637812,0.0007399786,0.0002015478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003083561,0.0001601957,0.06231719,0.001712263,0.0009762034,0.002040274,0.001574693,0.03422269,0.06813627,0.006955382,0.1275477,0.6912737],"study_design_scores_gemma":[0.0005450664,0.0004782351,0.0649887,0.0004282422,0.0003140619,0.005322962,0.0003481169,0.7068475,0.1265243,0.01680064,0.0770032,0.0003990625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05448432,0.0008077599,0.732552,0.0006509864,0.0001648,0.0003546008,0.01407355,0.1934855,0.003426498],"genre_scores_gemma":[0.2457055,0.0006378172,0.7217223,0.0005203304,0.0001206969,0.001130962,0.01264442,0.01440437,0.003113571],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01251932,"threshold_uncertainty_score":0.04188132,"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."}}