The human disease network

Goh, K.-I.; Cusick, Michael E.; Valle, David; Childs, Barton; Vidal, Marc; Barabási, Albert László

doi:10.1073/pnas.0701361104

Cited by 2,956 publications

(3,003 citation statements)

References 32 publications

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“…They often located in the functional the topological periphery of the complex biological networks [21]. Based on the hypothesis above, the T2D-related seed gene set was compared with a set of Housekeeping genes and a set of cancer genes.…”

Section: Resultsmentioning

confidence: 99%

Detection of type 2 diabetes related modules and genes based on epigenetic networks

et al. 2014

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BackgroundType 2 diabetes (T2D) is one of the most common chronic metabolic diseases characterized by insulin resistance and the decrease of insulin secretion. Genetic variation can only explain part of the heritability of T2D, so there need new methods to detect the susceptibility genes of the disease. Epigenetics could establish the interface between the environmental factor and the T2D Pathological mechanism.ResultsBased on the network theory and by combining epigenetic characteristics with human interactome, the weighted human DNA methylation network (WMPN) was constructed, and a T2D-related subnetwork (TMSN) was obtained through T2D-related differentially methylated genes. It is found that TMSN had a T2D specific network structure that non-fatal metabolic disease causing genes were often located in the topological and functional periphery of network. Combined with chromatin modifications, the weighted chromatin modification network (WCPN) was built, and a T2D-related chromatin modification pattern subnetwork was obtained by the TMSN gene set. TCSN had a densely connected network community, indicating that TMSN and TCSN could represent a collection of T2D-related epigenetic dysregulated sub-pathways. Using the cumulative hypergeometric test, 24 interplay modules of DNA methylation and chromatin modifications were identified. By the analysis of gene expression in human T2D islet tissue, it is found that there existed genes with the variant expression level caused by the aberrant DNA methylation and (or) chromatin modifications, which might affect and promote the development of T2D.ConclusionsHere we have detected the potential interplay modules of DNA methylation and chromatin modifications for T2D. The study of T2D epigenetic networks provides a new way for understanding the pathogenic mechanism of T2D caused by epigenetic disorders.

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Section: Resultsmentioning

confidence: 99%

Detection of type 2 diabetes related modules and genes based on epigenetic networks

et al. 2014

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“…Even phenotypes often cluster. Comorbidities, the over proportional co‐occurrence of diseases, were shown to affect many diseases possibly through shared underlying mechanisms (Goh et al ., 2007). …”

Section: From Omics To Systems Biologymentioning

confidence: 99%

“…One example for a phenotypic network was created by Goh et al . (2007) using diseases as nodes and connecting diseases with shared genetic risk factor by edges (cf. Fig.…”

Section: From Omics To Systems Biologymentioning

confidence: 99%

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Integration of ‘omics’ data in aging research: from biomarkers to systems biology

et al. 2015

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SummaryAge is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age‐related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high‐throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so‐called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age‐related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

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“…These efforts revealed the ubiquity of general architectural features shared by social networks, power grids, brain neural networks, and networks of the cell. It is now commonplace to use networks to assemble whole genomes using de Bruijn graphs [20], to study whole transcriptomes [21], whole proteomes [22], and whole metabolomes [23], and represent drug-protein target interactions as networks [24], and the genic basis of illnesses using diseasome networks [25]. Reductionism gives way to holism.…”

Section: Introductionmentioning

confidence: 99%

The curriculum prerequisite network: Modeling the curriculum as a complex system

Aldrich

2015

Biochem Molecular Bio Educ

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This article advances the prerequisite network as a means to visualize the hidden structure in an academic curriculum. Networks have been used to represent a variety of complex systems ranging from social systems to biochemical pathways and protein interactions. Here, I treat the academic curriculum as a complex system with nodes representing courses and links between nodes the course prerequisites as readily obtained from a course catalogue. I show that the catalogue data can be rendered as a directed acyclic graph, which has certain desirable analytical features. Using metrics developed in mathematical graph theory, I characterize the overall structure of the undergraduate curriculum of Benedictine University along with that of its Biochemistry and Molecular Biology program. The latter program is shown to contain hidden community structure that crosses disciplinary boundaries. The overall curriculum is seen as partitioned into numerous isolated course groupings, the size of the groups varying considerably. Individual courses serve different roles in the organization, such as information sources, hubs, and bridges. The curriculum prerequisite network represents the intrinsic, hard-wired constraints on the flow of information in a curriculum, and is the organizational context within which learning occurs. I explore some applications for advising and curriculum reform.

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The human disease network

Cited by 2,956 publications

References 32 publications

Detection of type 2 diabetes related modules and genes based on epigenetic networks

Detection of type 2 diabetes related modules and genes based on epigenetic networks

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

The curriculum prerequisite network: Modeling the curriculum as a complex system

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