{"id":"W2971384141","doi":"10.1038/s41597-019-0174-7","title":"Creating reproducible pharmacogenomic analysis pipelines","year":2019,"lang":"en","type":"article","venue":"Scientific Data","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Institute of Cancer Research; Ontario Institute for Cancer Research; University Health Network; University of Toronto; Princess Margaret Cancer Centre","funders":"","keywords":"Workflow; Computer science; Pipeline (software); Scalability; Identifier; Pharmacogenomics; Data science; Process (computing); Pipeline transport; Metadata; Data access; Data mining; Database; Bioinformatics; World Wide Web; Engineering; Biology; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04398284,0.001175096,0.001571592,0.004635633,0.001954003,0.008135394,0.004047741,0.001640363,0.004417684],"category_scores_gemma":[0.07230221,0.001755219,0.003797158,0.00528507,0.00284266,0.006882894,0.00841186,0.004297274,0.003278712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003459668,"about_ca_system_score_gemma":0.0167372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005033827,"about_ca_topic_score_gemma":0.004580145,"domain_scores_codex":[0.9832278,0.003938603,0.002689123,0.003389529,0.005732147,0.001022751],"domain_scores_gemma":[0.9483531,0.01373948,0.003487132,0.02133512,0.011568,0.001517252],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001838662,0.000982389,0.03206832,0.001815206,0.001151906,0.002018335,0.002517264,0.1275276,0.07834107,0.2219639,0.08317779,0.4465975],"study_design_scores_gemma":[0.0007119541,0.0003438267,0.006825556,0.0003781045,0.0003672019,0.0005357172,0.0006627383,0.2962005,0.1376497,0.3041128,0.2517051,0.0005069374],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01113922,0.0002517861,0.9542553,0.00175637,0.0002036029,0.001051946,0.006469581,0.02197494,0.002897309],"genre_scores_gemma":[0.09039836,0.0007398162,0.8816394,0.0006741353,0.0001362598,0.001793479,0.01708039,0.005631137,0.001907089],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9560171,"threshold_uncertainty_score":0.2326062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2945933816599378,"score_gpt":0.4549622130902566,"score_spread":0.1603688314303188,"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."}}