{"id":"W4411754998","doi":"10.1093/nargab/lqaf087","title":"MOLGENIS VIP: an end-to-end DNA variant interpretation pipeline for research and diagnostics configurable to support rapid implementation of new methods","year":2025,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Scalability; Pipeline (software); Computer science; Data science; DNA sequencing; Genome; Variety (cybernetics); Software; Protocol (science); Computational biology; Data mining; Biology; Medicine; Artificial intelligence; Genetics; Database; Gene","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009796494,0.0001465238,0.0002264815,0.0001641789,0.0001424209,0.00006401438,0.0001418879,0.00009843807,0.000009881399],"category_scores_gemma":[0.0001482685,0.0001410034,0.00003733937,0.0001351715,0.00006114475,0.000004443714,0.0002073816,0.00005480216,9.726253e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002208491,"about_ca_system_score_gemma":0.00022589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008162114,"about_ca_topic_score_gemma":0.000103635,"domain_scores_codex":[0.9988933,0.00003820696,0.0004906295,0.0002230299,0.00009135903,0.0002634795],"domain_scores_gemma":[0.9991115,0.0001073642,0.000100389,0.0002357344,0.0002883198,0.0001566723],"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":[0.0002346708,0.00004189177,0.000280063,0.0001558227,0.000131582,2.93627e-7,0.003562842,0.00006749311,0.4544094,0.001030477,0.005246486,0.534839],"study_design_scores_gemma":[0.002411844,0.005629057,0.003577775,0.00004613729,0.0001724081,0.00001527891,0.009866436,0.0110947,0.7267193,0.003407957,0.2365142,0.0005448522],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7813899,0.0008893271,0.2146468,0.0006238882,0.0002465516,0.001238215,0.0004492069,0.000003680369,0.0005123971],"genre_scores_gemma":[0.7248366,0.002648905,0.2707936,0.0008577693,0.0001158703,0.00006415499,0.0002872321,0.00002134206,0.0003745793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5342941,"threshold_uncertainty_score":0.574995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04216451958384585,"score_gpt":0.3995709628316272,"score_spread":0.3574064432477814,"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."}}