{"id":"W4401435285","doi":"10.1101/2024.08.06.606224","title":"VUStruct: A compute pipeline for high throughput and personalized structural biology","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"U.S. National Library of Medicine; National Cancer Institute; NIH Office of the Director; National Institute on Drug Abuse; National Heart, Lung, and Blood Institute; National Institutes of Health; National Human Genome Research Institute; National Institute of Neurological Disorders and Stroke; Bundesministerium für Bildung und Forschung; National Institute on Aging; Deutsche Forschungsgemeinschaft; German Network for Bioinformatics Infrastructure; Deutscher Akademischer Austauschdienst; Vanderbilt University; National Institute of Allergy and Infectious Diseases; Alexander von Humboldt-Stiftung","keywords":"Pipeline (software); Throughput; Computer science; Computational biology; Computer architecture; Biology; Parallel computing; Programming language; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.001592183,0.002052239,0.0009952134,0.001330213,0.001046326,0.002844236,0.003757152,0.001254837,0.02826733],"category_scores_gemma":[0.005523881,0.001134407,0.002013601,0.001493395,0.0009028745,0.002662463,0.003350293,0.002980134,0.01194812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001187988,"about_ca_system_score_gemma":0.002984335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005784079,"about_ca_topic_score_gemma":0.00870621,"domain_scores_codex":[0.9992589,0.0001134176,0.00005573936,0.0001601433,0.0003291953,0.00008244187],"domain_scores_gemma":[0.9986604,0.0005308867,0.00008958046,0.000266812,0.0002896247,0.0001626625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00112347,0.0002302663,0.007071571,0.001232991,0.0004943365,0.0009071742,0.0006209214,0.05333468,0.01518983,0.05411162,0.581495,0.2841882],"study_design_scores_gemma":[0.0005446255,0.00018166,0.002977433,0.0002168346,0.0001442877,0.000533721,0.0001998161,0.5331234,0.02370878,0.1391191,0.2989899,0.0002605813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.007562148,0.001265449,0.6362905,0.001248849,0.0005004081,0.0003099357,0.01405175,0.3239301,0.01484096],"genre_scores_gemma":[0.1412446,0.002625915,0.6957794,0.001924889,0.0002972257,0.00117192,0.06935254,0.07531841,0.01228514],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02826733,"threshold_uncertainty_score":0.0945636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00956422395770509,"score_gpt":0.2320111574831627,"score_spread":0.2224469335254576,"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."}}