系统生物学和基于网络的计算模型方法在神经疾病生物标志物发现中的应用

N. Srivastava, P. Srivastava
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引用次数: 0

摘要

神经退行性疾病是导致进行性退行性变的无法治愈和丧失能力的疾病。从系统的角度很难定量或有意义地定义神经系统的复杂性。因此,随着开发新的有效治疗干预措施的进展,了解神经系统及其复杂分子相互作用的潜在分子机制和意义是重要的。生物标志物的发现是神经系统疾病早期诊断、预后和新疗法监测的重要需求。系统生物学和基于网络的计算模型方法的出现提供了疾病及其复杂分子相互作用的潜在分子机制和意义。因此,理解神经系统的具体性质变得非常容易,因为它在整合多个层面的组学数据方面发挥着重要作用,从而在开发更准确、更有效的神经疾病生物标志物方面取得了关键成功。目前的综述集中在系统生物学和基于网络的计算模型方法在生物标志物发现中的重要贡献,特别是在神经疾病方面。
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System Biology and Network-Based Computational Model Approaches in Biomarker Discovery in Reference to Neurological Disorder
Neurodegenerative diseases are irredeemable and incapacitating conditions that result in progressive degeneration. It is difficult to define the complexity of neuro-system quantitatively or meaningfully from a system standpoint. Thus, inclined towards the progress in developing new and effective therapeutic intervention, it is important to understand the underlying molecular mechanism and significance of neuro system and their complex molecular interaction. A biomarker discovery is an important need for early disease diagnosis, prognosis and monitoring of new therapy for neurological disorders. The emergence of system biology and network-based computational model approaches provides the underlying molecular mechanism and significance of disease and their complex molecular interaction. Thus, it becomes quite easy to understand the specific nature of neuro system as well as it plays a significant role in integrating the omics data at multiple levels that lead to key success in the development of more accurate and efficient biomarker for neurological disorders. The current review focused on significant contributions of system biology and network-based computational model approaches in biomarker discovery with special reference to neurological disorders.
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