[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128115-en":3,"doc-seo-128115-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128115,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents","Machine learning workflows were used to connect gene expression patterns with neurological disorders triggered by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents. Transcriptomic datasets from TBI, EEV, and OPNA injuries were collected and normalized across multiple platforms, then integrated to discover both shared and condition-specific molecular signatures. Deep neural networks extracted association signals to predict distinct neurological disorders from VEEV, OPNA, and TBI samples, while gene ontology and pathway analyses linked gene product functions to disease-relevant biology and potential countermeasure targets.","TYPE Original Research PUBLISHED 13 May 2025  \nDOI 10.3389/fncom.2025.1529902  \nOPEN ACCESS  \nEDITED BY  \nHassene Seddik,  \nUniversity of Tunis/RIFTSI Laboratory (Smart Robotic, Friability and Signal and Image Processing Research Laboratory), Tunisia  \nREVIEWED BY  \nGiorgos Livanos,  \nTechnical University of Crete, Greece Mohammad Khubeb Siddiqui,  \nSaudi Arabia Basic Industries, Saudi Arabia  \n*CORRESPONDENCE  \nXiaowei Wu  \n [xwwu@vt.edu](xwwu@vt.edu)[ ](xwwu@vt.edu)Hehuang Xie  \n [davidxie@vt.edu](davidxie@vt.edu)[ ](davidxie@vt.edu)RECEIVED 18 November 2024 ACCEPTED 29 April 2025 PUBLISHED 13 May 2025  \nCITATION  \nYin L, VanderGiessen M, Kumar V, Conacher B, Chao P-CH, Theus M, Johnson E, Kehn-Hall K, Wu X and Xie H (2025) Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents.  \nFront. Comput. Neurosci. 19:1529902 .  \ndoi: 10.3389/fncom.2025.1529902  \nCOPYRIGHT  \n© 2025 Yin, VanderGiessen, Kumar, Conacher, Chao, Theus, Johnson, Kehn-Hall, Wu and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents  \nLiduo Yin 1, Morgen VanderGiessen 1,2, Vinoth Kumar 1, Benjamin Conacher 1, Po-Chien Haku Chao 1, Michelle Theus 1, Erik Johnson3, Kylene Kehn-Hall 1,2, Xiaowei Wu4* and Hehuang Xie 1*  \n1 Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, 2Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, 3 Neuroscience Department, Medical Toxicology Division, U. S. Army Medical Research Institute of Chemical Defense, Aberdeen, MD, United States, 4 Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States  \nVenezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and pose a significant threat to the civilian population and the warfighter. Likewise, organophosphorus nerve agents (OPNA) are highly toxic chemicals that pose serious health threats of neurological deficits to both military and civilian personnel around the world. Consequently, only a select few approved research groups are permitted to study these dangerous chemical and biological warfare agents. This has created a significant gap in our scientific understanding of the mechanisms underlying neurological diseases. Valuable insights may be gleaned by drawing parallels to other extensively researched neuropathologies, such as traumatic brain injuries (TBI) . By examining combined gene expression profiles, common and unique molecular characteristics may be discovered, providing new insights into medical countermeasures (MCMs) for TBI, EEV infection and OPNA neuropathologies and sequelae. In this study, we collected transcriptomic datasets for neurological disorders caused by TBI, EEV, and OPNA injury, and implemented a framework to normalize and integrate gene expression datasets derived from various platforms. Effective machine learning approaches were developed to identify critical genes that are either shared by or distinctive among the three neuropathologies. With the aid of deep neural networks, we were able to extract important ass","cbCaig1qrMgQdWFi","https://ap.wps.com/l/cbCaig1qrMgQdWFi","pdf",1238823,3,1,9,"English","en",105,"# Introduction\n# Methods\n## Data collection and preprocessing\n## Integrated machine learning framework\n# Results\n## Gene discovery and shared vs distinctive signatures\n## Deep neural network predictions\n## Gene ontology and pathway analysis\n# Discussion\n## Implications for biomarkers and neuroprotective targets\n# Conclusion","[{\"question\":\"What neurological conditions are analyzed in this study?\",\"answer\":\"The study analyzes neurological disorders caused by traumatic brain injuries, equine encephalitis viruses, and organophosphorus nerve agents, using integrated transcriptomic datasets.\"},{\"question\":\"How are gene expression datasets handled for machine learning?\",\"answer\":\"Transcriptomic datasets from different platforms are normalized and integrated into a unified framework before training machine learning models.\"},{\"question\":\"What biological insight is obtained beyond gene prediction?\",\"answer\":\"Gene ontology and pathway analyses identify neuropathologic features tied to specific gene product attributes and functions, clarifying underlying disease biology.\"}]","Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents | 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neurological conditions are analyzed in this study?","Question",{"text":76,"@type":77},"The study analyzes neurological disorders caused by traumatic brain injuries, equine encephalitis viruses, and organophosphorus nerve agents, using integrated transcriptomic datasets.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are gene expression datasets handled for machine learning?",{"text":81,"@type":77},"Transcriptomic datasets from different platforms are normalized and integrated into a unified framework before training machine learning models.",{"name":83,"@type":74,"acceptedAnswer":84},"What biological insight is obtained beyond gene prediction?",{"text":85,"@type":77},"Gene ontology and pathway analyses identify neuropathologic features tied to specific gene product attributes and functions, clarifying underlying disease 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