{"id":"W2167432612","doi":"10.1186/1471-2105-16-s11-s2","title":"VisRseq: R-based visual framework for analysis of sequencing data","year":2015,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Genome British Columbia; Michael Smith Health Research BC","keywords":"Computer science; Workflow; Usability; Interactivity; Bioconductor; Set (abstract data type); Data science; Visualization; Graphical user interface; Software engineering; World Wide Web; Data mining; Human–computer interaction; Programming language; 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":[0.0003594172,0.0001185342,0.0002329987,0.00009078417,0.00004050841,0.00001696627,0.0003305839,0.000114823,0.00000223885],"category_scores_gemma":[0.0004069914,0.0001069105,0.0001127957,0.0002241383,0.00006036396,0.000001780388,0.0002033042,0.00003401878,0.000001901086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001431067,"about_ca_system_score_gemma":0.000263836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001095494,"about_ca_topic_score_gemma":0.00004520471,"domain_scores_codex":[0.9991837,0.00001456628,0.0003524674,0.0001525041,0.0001235988,0.0001731659],"domain_scores_gemma":[0.9988641,0.00005555742,0.000193648,0.0006409821,0.0001698535,0.00007586555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002184259,0.0009288529,0.2755609,0.002667351,0.01714069,0.000002321917,0.00564273,0.4877961,0.1438719,0.01102715,0.02491269,0.02826504],"study_design_scores_gemma":[0.0006990745,0.000422382,0.001682643,0.00001611693,0.0008353319,8.597966e-7,0.001397313,0.9709133,0.01515377,0.0003193482,0.008274679,0.0002852221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2448968,0.0003237822,0.7537481,0.00001996011,0.0001173474,0.0001910179,0.0004392205,0.000003855228,0.0002599264],"genre_scores_gemma":[0.4544964,0.00002118545,0.5443218,0.0001850086,0.0000809726,0.00001057072,0.0008477215,0.00001123025,0.00002520224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4831172,"threshold_uncertainty_score":0.4359682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1262815700190899,"score_gpt":0.352157416540137,"score_spread":0.2258758465210471,"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."}}