Integrative multi-omics and systems bioinformatics in translational neuroscience:A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Opportunities for Computational Techniques for Multi-Omics Integrated Personalized Medicine

Personalized medicine is defined as ＂a model of healthcare that is predictive, personalized, preventive,and participator＂ and has very broad content. With the rapid development of high-throughput technologies, an explosive accumulation of biological information is collected from multiple layers of biological processes, including genomics, transcriptomics, proteomics, metabonomics, and interactomics（omics）. Implementing integrative analysis of these multiple omics data is the best way of deriving systematical and comprehensive views of living organisms, achieving better understanding of disease mechanisms, and finding operable personalized health treatments. With the help of computational methods, research in the field of biology and biomedicine has gained tremendous benefits over the past few decades. In the new era of personalized medicine, we will rely more on the assistance of computational analysis. In this paper, we briefly review the generation of multiple omics and their basic characteristics. And then the challenges and opportunities for computational analysis are discussed and some state-of-art analysis methods that were recently proposed by peers for integrative analysis of multiple omics data are reviewed. We foresee that further integrated omics data platform and computational tools would help to translate the biological knowledge to clinical usage and accelerate development of personalized medicine.

Chinese Integrative Medicine:Translation toward PersonCentered and Balanced Medicine

Chinese integrative medicine （CIM） focuses on the integration of conventional medicine （biomedicine） with Chinese medicine （CM）. Although the CIM field has witnessed several advancements, the definition and classification of CIM is not quite clear, given that an independent theory system has not yet been established in this field. Therefore, future research and studies should focus on the following objectives： （1） emphasizing CM features, （2） improving CIM positioning, and （3） establishing CIM standards. These concerted efforts will help CIM be at par with international standards and criteria. With the development of CIM, the world will embrace a new medical system providing person-cantered treatment with a balanced medicine approach.

Translational Bioinformatics: Past, Present, and Future

Though a relatively young discipline, translational bioinformatics （TBI） has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contex- tualization of the term TBI, describes the discipline＇s brief history and past accomplishments, as well as current loci, and concludes with predictions of future directions in the field.

生物信息学与转化医学研究

第一份人类基因组草图的完成给生物医学带来了革命性的变化。随着近年来生物技术的迅速发展和电子健康档案在临床医学的广泛应用,生物信息学作为一门专注于生物医学数据处理的学科变得越来越重要,尤其是它能促进生物医学知识的转化从而提高临床实践和医疗保健。本文将重点介绍与基础生物学和临床医学有关的数据资源,以及如何利用生物信息学来整合相关信息,从而促进转化医学的研究和实践。

转化生物信息学发展的路径分析

DNA和RNA测序、基因芯片、高通量的蛋白质组学和代谢组学技术的普遍使用,需要新方法来把这些新型数据转化成新信息,然后把新信息转化成新知识。随着生物医学信息学和转化医学的快速发展,生物医学信息学逐渐成为转化医学跨学科、跨领域团队信息沟通和知识转化的重要基础,两学科出现了交叉融合趋势。在此背景下,转化生物信息学应运而生。作者就转化生物信息学学科发展历史、定义及主要研究内容和热点进行论述。