Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network  

作　　者：Olfat Al-Harazi Sadiq Al Insaif Monirah A.Al-Ajlan Namik Kaya Nduna Dzimiri Dilek Colak 

机构地区：[1]Department of Biostatistics,Epidemiology and Scientific Computing,King Faisal Specialist Hospital and Research Centre [2]College of Computer and Information Sciences,King Saud University [3]Department of Genetics,King Faisal Specialist Hospital and Research Centre

出　　处：《Journal of Genetics and Genomics》2016年第6期349-367,共19页遗传学报（英文版）

基　　金：funded by the National Plan for Science,Technology and Innovation program (NSTIP/KACST, No.11-BIO2072-20 to D.C.)

摘　　要：A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network（interactome） indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy（IDCM） data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network（interactome） indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy（IDCM） data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

关 键 词：Proteineprotein interaction Subnetwork marker Diseasome Network medicine Systems biology Genomics 

分 类 号：R346[医药卫生—基础医学]