Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data

Eblen, John D.; Gerling, Ivan; Saxton, Arnold M.; Wu, Jian; Snoddy, Jay; Langston, Michael A.

doi:10.1142/9789812771667_0010

Cited by 15 publications

(14 citation statements)

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“… Subgraph Overlap may increase fidelity. By tuning our codes to provide this feature, a vertex may reside in more than one subgraph, just as a variable may be involved in more than one relationship [ 26 ]. Domain Knowledge , usually invoked only at toolchain extremes, can sometimes be applied midstream.…”

Section: Refinements and Variationsmentioning

confidence: 99%

Scalable Combinatorial Tools for Health Disparities Research

Langston

Levine

Kilbourne

et al. 2014

IJERPH

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Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual's genetic endowment, particularly to environmental exposures experienced across his or her life course. New analytical approaches are clearly required as investigators turn to complicated systems theory and ecological, place-based and life-history perspectives in order to understand more clearly the relationships between social determinants, environmental exposures and health disparities. While traditional data analysis techniques remain foundational to health disparities research, they are easily overwhelmed by the ever-increasing size and heterogeneity of available data needed to illuminate latent gene x environment interactions. This has prompted the adaptation and application of scalable combinatorial methods, many from genome science research, to the study of population health. Most of these powerful tools are algorithmically sophisticated, highly automated and mathematically abstract. Their utility motivates the main theme of this paper, which is to describe real applications of innovative transdisciplinary models and analyses in an effort to help move the research community closer toward identifying the causal mechanisms and associated environmental contexts underlying health disparities. The public health exposome is used as a contemporary focus for addressing the complex nature of this subject.Keywords: combinatorial algorithms; data science; graph theoretical techniques; health disparities research; heterogeneous data analysis; high performance computing; public health exposome; relevance networks; scalable computation BackgroundResearch on health disparities in racial/ethnic and disadvantaged groups has received increased attention in recent years. More and better data, new analysis techniques, and focused research programs have helped increase our understanding of issues and potential solutions. One such program, initiated by the authors of this exposition, is the National Health Disparities Research Center of Excellence (HDRCOE), begun over a decade ago at Meharry Medical College in Nashville, Tennessee. Supported by the National Institute on Minority Health and Health Disparities, the HDRCOE is an inter-institutional, inter-disciplinary center designed to study the complex relationships between human factors, community context and macro social forces that may lead to health disparities.A newly established center, with which this paper's authors are also intrinsically affiliated, is the Research Center on Health Disparities, Equity, and the Exposome (RCHDEE) at the University of Tennessee Health Science Center in Memphis, Tennessee. The RCHDEE is a collaboration that brings together academic and community partners to conduct health disparities research in an effort to promote healthy neighborhoods and help eliminate disparities.The public health exposome model [1] identifies enviro...

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Section: Refinements and Variationsmentioning

confidence: 99%

Scalable Combinatorial Tools for Health Disparities Research

Langston

Levine

Kilbourne

et al. 2014

IJERPH

View full text Add to dashboard Cite

show abstract

“…Representative examples include [8, 9, 10]. Instead, our primary goal in this paper is to investigate paraclique’s theoretical basis.…”

Section: Introductionmentioning

confidence: 99%

Lower bounds on paraclique density

Hagan

Langston

Wang

2016

Discrete Applied Mathematics

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The scientific literature teems with clique-centric clustering strategies. In this paper we analyze one such method, the paraclique algorithm. Paraclique has found practical utility in a variety of application domains, and has been successfully employed to reduce the effects of noise. Nevertheless, its formal analysis and worst-case guarantees have remained elusive. We address this issue by deriving a series of lower bounds on paraclique densities.

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“…But yeast has roughly 6,000 proteins indicating that there might be other protein complexes. It is stated in [18] that edge density is the most telling statistical clustering metric, and it is easy to realize that the higher the density of a subgraph the more cohesive the nodes. It can be hypothesized that comprehensive determination of high-density subgraphs can give clues to comprehensive mapping of complexes and might be helpful for deciphering other biological information.…”

Section: Introductionmentioning

confidence: 99%

Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

Altaf-Ul-Amin

Wada

Kanaya

2012

ISRN Biomathematics

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This paper presents an algorithm called DPClusO for partitioning simple graphs into overlapping modules, that is, clusters constrained by density and periphery tracking. The major advantages of DPClusO over the related and previously published algorithm DPClus are shorter running time and ensuring coverage, that is, each node goes to at least one module. DPClusO is a general-purpose clustering algorithm and useful for finding overlapping cohesive groups in a simple graph for any type of application. This work shows that the modules generated by DPClusO from several PPI networks of yeast with high-density constraint match with more known complexes compared to some other recently published complex generating algorithms. Furthermore, the biological significance of the high density modules has been demonstrated by comparing their P values in the context of Gene Ontology (GO) terms with those of the randomly generated modules having the same size, distribution, and zero density. As a consequence, it was also learnt that a PPI network is a combination of mainly high-density and star-like modules.

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Graph Algorithms for Integrated Biological Analysis, with Applications to Type 1 Diabetes Data

Cited by 15 publications

References 0 publications

Scalable Combinatorial Tools for Health Disparities Research

Scalable Combinatorial Tools for Health Disparities Research

Lower bounds on paraclique density

Partitioning a PPI Network into Overlapping Modules Constrained by High-Density and Periphery Tracking

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