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精神神经免疫学中的转化生物信息学:方法与应用

Translational bioinformatics in psychoneuroimmunology: methods and applications.

作者信息

Yan Qing

机构信息

PharmTao, Santa Clara, CA, USA.

出版信息

Methods Mol Biol. 2012;934:383-400. doi: 10.1007/978-1-62703-071-7_20.

Abstract

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.

摘要

转化生物信息学在将心理神经免疫学(PNI)转化为个性化医学方面发挥着不可或缺的作用。它提供了一种强大的方法来弥合PNI和系统生物学中各个知识领域之间的差距。不同系统层面的转化生物信息学方法可以促进模式识别,并加速和验证系统性生物标志物的发现,以便将其纳入临床试验和结果评估。对包括基于行为的特征在内的基因型和表型之间的相关性进行分析,将有助于从基于疾病的医学向以患者为中心的医学转变。转化生物信息学还将能够建立患者对疾病、疫苗和药物反应的预测模型。在PNI研究中,诸如神经元模型等系统生物学模型的开发将发挥关键作用。基于数据整合、数据挖掘和知识表示的方法是构建诸如电子健康记录和计算机化决策支持系统等健康信息系统的基本要素。广泛的PNI研究需要对基因、病理生理学和行为进行数据整合。诸如基于网络的系统生物学方法等知识发现方法在研究诸如阿尔茨海默病等疾病所涉及的各个脑区的通路之间的相互作用方面很有价值。

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