{"id":"W2968815841","doi":"10.3389/fgene.2019.00856","title":"Evaluation of the Performance of AmpliSeq and SureSelect Exome Sequencing Libraries for Ion Proton","year":2019,"lang":"en","type":"article","venue":"Frontiers in Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council; Medical Research Council; Canadian Institutes of Health Research; Oesterreichische Nationalbank; Medizinische Universität Graz; Karl-Franzens-Universität Graz; Bundesministerium für Wissenschaft, Forschung und Wirtschaft; Bundesministerium für Bildung und Forschung; Austrian Science Fund; ZonMw; Agence Nationale de la Recherche; European Commission","keywords":"Exome sequencing; Concordance; Ion semiconductor sequencing; Computational biology; Exome; Biology; Pipeline (software); Predictive value; DNA sequencing; Genetics; Gene; Mutation; Computer science; Medicine; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01027736,0.001443258,0.001014247,0.001737152,0.0007110223,0.001675717,0.00137024,0.001554724,0.002236815],"category_scores_gemma":[0.02134417,0.0007285866,0.00128993,0.001153344,0.0007962355,0.001022349,0.001200848,0.001027033,0.001532725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007177469,"about_ca_system_score_gemma":0.0009240514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001831145,"about_ca_topic_score_gemma":0.002407393,"domain_scores_codex":[0.9903672,0.002380969,0.0009274881,0.002140153,0.003577223,0.0006070072],"domain_scores_gemma":[0.9886407,0.006616127,0.000769406,0.001308588,0.002417971,0.0002473258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001764691,0.0003499481,0.02323984,0.001126164,0.0007170977,0.0003190991,0.0006832025,0.01188728,0.8553298,0.0007904955,0.001735806,0.1020566],"study_design_scores_gemma":[0.00006899038,0.001505577,0.04231416,0.00008433964,0.0004410724,0.0009708249,0.0001346388,0.02431702,0.9189763,0.0006316618,0.01038502,0.0001703579],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7356815,0.005080475,0.2427661,0.0008718816,0.0002439977,0.001173227,0.007402905,0.003056912,0.003723149],"genre_scores_gemma":[0.6478133,0.00222056,0.3270492,0.0006021072,0.0001129314,0.001384483,0.01472696,0.001445606,0.004644877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01027736,"threshold_uncertainty_score":0.05435252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885592385069608,"score_gpt":0.2371262269403756,"score_spread":0.2182703030896795,"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."}}