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Science China(Life Sciences) 2011年第2期54卷 189-193页

作者： YAO Chen ZHANG Min ZOU JinFeng LI HongDong WANG Dong ZHU Jing GUO Zheng Bioinformatlcs Centre School of Life Science University of Electronic Science and Technology of China Chengdu 610054 China College of Bioinformatics Harbin Medical University Harbin 150086 China

Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including ***,the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases,which would introduce low reproducibility of gene lists selected by statistical ***,by analyzing seven cancer datasets,we showed that,in each cancer,a wide range of functional modules have altered gene expressions and thus have high disease classification *** results also showed that seven modules are shared across diverse cancers,suggesting hints about the common mechanisms of ***,instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments,we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.

关键词： differentially expressed gene functional module classification diverse cancers

Identification of Risk Pathways and functional modules for Coronary Artery Disease Based on Genome-wide SNP Data

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Genomics, Proteomics & Bioinformatics 2016年第6期14卷 349-356页

作者： Xiang Zhao Yi-Zhao Luan Xiaoyu Zuo Ye-Da Chen Jiheng Qin Lv Jin Yiqing Tan Meihua Lin Naizun Zhang Yan Liang Shao-Qi Rao Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology School of Public HealthGuangdong Medical CollegeDongguan 523808China School of Life Sciences Sun Yat-sen UniversityGuangzhou 510080China Department of Medical Statistics and Epidemiology School of Public HealthSun Yat-sen UniversityGuangzhou 510080China Maoming People's Hospital Maoming 525000China

Coronary artery disease （CAD） is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism （SNP） profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium （WTCCC） SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene-gene interactions involved in these susceptible pathways with their protein protein interaction （PPI） knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer＇s disease, non-alcoholic fatty liver disease, and Huntington＇s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer＇s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer＇s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

关键词： Coronary artery disease Genome-wide SNP profiling Risk pathway functional module Genetic network

Discovering the cellular-localized functional modules and modular interactions in response to liver cancer

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High Technology Letters 2008年第4期14卷 437-442页

作者：朱晶 Guo Zheng Yang Da Zhang Min Wang Jing Wang Chenguang School of Life Science and Bioinformatics Centre University of Electronic Science and Technology of China Chengdu 610054 P. R. China Department of Bioinformatics Bio-pharmaceutical Key Laboratory of Heilongjiang Province-Incubator of State Key Laboratory Harbin Medical University Harbin 150086 P. R. China

In this paper, we firstly identify the functional modules enriched with differentially expressed genes （DEGs） and characterized by biological processes in specific cellular locations, based on gene ontology （GO） and microarray data. Then, we further define and filter disease relevant signature modules according to the ranking of the disease discriminating abilities of the pre-selected functional modules. At last, we analyze the potential way by which they cooperate towards human disease. Application of the proposed method to the analysis of a liver cancer dataset shows that, using the same false discovery rate （ FDR ） threshold, we can find more biologically meaningful and detailed processes by using the cellular localization information. Some biological evidences support the relevancy of our biological modules to the disease mechanism.

关键词： microarray Gene Ontology functional module cellular location liver cancer

Interaction of cellular-localized signature modules in response to prostate cancer

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Progress in Natural Science:Materials International 2007年第11期17卷 1368-1375页

作者： Zhu Jing1, Guo Zheng1,2, Zhang Min2, Yang Da2, Wang Jing2 and Wang Chenguang2(1. School of Life Science and Bioinformatics Centre, University of Electronic Science and Technology of China, Chengdu 610054, China 2. Department of Bioinformatics, Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Harbin 150081, China) School of Life Science and Bioinformatics Centre University of Electronic Science and Technology of China Chengdu 610054 China Department of Bioinformatics Bio-Pharmaceutical Key Laboratory of Heilongjiang Province， Harbin Medical University， Harbin 150081 China

Rapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge makes it promising for systematic understanding of complex human diseases at the functional modules level. Current modular categorizations can be defined and selected more specifically and precisely in terms of both biological processes and cellular locations, aiming at uncovering the modular molecular networks highly relevant to cancers. Based on Gene Ontology, we identifed the functional modules enriched with differentially expressed genes and characterized by biological processes and specific cellular locations. Then, according to the ranking of the disease discriminating abilities of the pre-selected functional modules, we further defined and filtered signature modules which have higher relevance to the cancer under study. Applications of the proposed method to the analysis of a prostate cancer dataset revealed insightful biological modules.

关键词： microarray analysis Gene Ontology classification analysis cellular location functional module

iBIG:An Integrative Network Tool for Supporting Human Disease Mechanism Studies

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Genomics, Proteomics & Bioinformatics 2013年第3期11卷 166-171页

作者： Jiya Sun Yuyun Pan Xuemei Feng Huijuan Zhang Yong Duan Hongxing Lei CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences Graduate University of Chinese Academy of Sciences UC Davis Genome Center and Department of Biomedical Engineering

Understanding the mechanism of complex human diseases is a major scientitic challenge. Towards this end, we developed a web-based network tool named iBIG （stands for integrative BioloGy）, which incorporates a variety of information on gene interaction and regulation. The generated network can be annotated with various types of information and visualized directly online. In addition to the gene networks based on physical and pathway interactions, networks at a functional level can also be constructed. Furthermore, a supplementary R package is provided to process microarray data and generate a list of important genes to be used as input for iBIG. To demonstrate its usefulness, we collected 54 microarrays on common human diseases including cancer, neurolog- ical disorders, infectious diseases and other common diseases. We processed the microarray data with our R package and constructed a network of functional modules perturbed in common human diseases. Networks at the functional level in combination with gene networks may provide new insight into the mechanism of human diseases, iBIG is freely available at http：//***/ibig.

关键词： Network functional module Disease mechanism Microarray Web server

Automated interpretation of metabolic capacity from genome and metagenome sequences

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Frontiers of Electrical and Electronic Engineering in China 2013年第3期8卷 192-200页

作者： Minoru Kanehisa Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Japan

The KEGG pathway maps are widely used as a reference data set for inferring high-level functions of the organism or the ecosystem from its genome or metagenome sequence data. The KEGG modules, which are tighter functional units often corresponding to subpathways in the KEGG pathway maps, are designed for better automation of genome interpretation. Each KEGG module is represented by a simple Boolean expression of KEGG Orthology （KO） identifiers （K numbers）, enabling automatic evaluation of the completeness of genes in the genome. Here we focus on metabolic functions and introduce reaction modules for improving annotation and signature modules for inferring metabolic capacity. We also describe how genome annotation is performed in KEGG using the manually created KO database and the computationaUy generated SSDB database. The resulting KEGG GENES database with KO （K number） annotation is a reference sequence database to be compared for automated annotation and interpretation of newly determined genomes.

关键词： metabolic pathway functional module genome annotation genome interpretation KEGG database

Exploring the evolution mechanism of function module in the biological networks

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Exploring the evolution mechanism of function module in the ...