机器学习识别与马脑炎病毒、创伤性脑损伤和有机磷神经毒剂引起的神经系统疾病相关的基因。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2025-05-13 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1529902
Liduo Yin, Morgen VanderGiessen, Vinoth Kumar, Benjamin Conacher, Po-Chien Haku Chao, Michelle Theus, Erik Johnson, Kylene Kehn-Hall, Xiaowei Wu, Hehuang Xie
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引用次数: 0

摘要

委内瑞拉、东部和西部马脑炎病毒(统称为马脑炎病毒——EEV)引起严重的神经系统疾病,并对平民和作战人员构成重大威胁。同样,有机磷神经毒剂(OPNA)是剧毒化学品,对世界各地的军事和文职人员造成严重的神经功能缺损的健康威胁。因此,只有少数经批准的研究小组被允许研究这些危险的化学和生物战剂。这在我们对神经系统疾病机制的科学理解上造成了一个重大的差距。通过与其他广泛研究的神经病理学,如创伤性脑损伤(TBI)的相似之处,可能会收集到有价值的见解。通过对基因表达谱的综合分析,可以发现共同和独特的分子特征,为TBI、EEV感染和OPNA神经病理及后遗症的医学对策(MCMs)提供新的见解。在这项研究中,我们收集了由TBI、EEV和OPNA损伤引起的神经系统疾病的转录组数据集,并实施了一个框架来标准化和整合来自不同平台的基因表达数据集。开发了有效的机器学习方法来识别三种神经病理学之间共享或独特的关键基因。在深度神经网络的帮助下,我们能够通过整合VEEV、OPNA和TBI样本的基因表达数据集提取重要的关联信号,从而准确预测不同神经系统疾病。基因本体论和通路分析进一步确定了具有特定基因产物属性和功能的神经病理特征,揭示了这些神经系统疾病的基础生物学。总的来说,我们强调了使用机器学习分析已发表的转录组数据的工作流程,该工作流程可用于识别特定神经系统疾病特有的基因生物标志物,以及多种神经病理学共享的基因。这些共享基因可以作为潜在的神经保护药物靶点,用于治疗EEV、TBI和OPNA等疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents.

Venezuelan, 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 association signals for accurate prediction of different neurological disorders by using integrated gene expression datasets of VEEV, OPNA, and TBI samples. Gene ontology and pathway analyses further identified neuropathologic features with specific gene product attributes and functions, shedding light on the fundamental biology of these neurological disorders. Collectively, we highlight a workflow to analyze published transcriptomic data using machine learning, which can be used for both identification of gene biomarkers that are unique to specific neurological conditions, as well as genes shared across multiple neuropathologies. These shared genes could serve as potential neuroprotective drug targets for conditions like EEV, TBI, and OPNA.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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