Numerical Analysis and Scientific Computing Commons^™

A Parallel Template For Implementing Filters For Biological Correlation Networks, Kathryn Dempsey Cooper, Vladimir Ufimtsev, Sanjukta Bhowmick, Hesham Ali

Interdisciplinary Informatics Faculty Publications

High throughput biological experiments are critical for their role in systems biology – the ability to survey the state of cellular mechanisms on the broad scale opens possibilities for the scientific researcher to understand how multiple components come together, and what goes wrong in disease states. However, the data returned from these experiments is massive and heterogeneous, and requires intuitive and clever computational algorithms for analysis. The correlation network model has been proposed as a tool for modeling and analysis of this high throughput data; structures within the model identified by graph theory have been found to represent key players …

On The Design Of Advanced Filters For Biological Networks Using Graph Theoretic Properties, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sanjukta Bhowmick, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Network modeling of biological systems is a powerful tool for analysis of high-throughput datasets by computational systems biologists. Integration of networks to form a heterogeneous model requires that each network be as noise-free as possible while still containing relevant biological information. In earlier work, we have shown that the graph theoretic properties of gene correlation networks can be used to highlight and maintain important structures such as high degree nodes, clusters, and critical links between sparse network branches while reducing noise. In this paper, we propose the design of advanced network filters using structurally related graph theoretic properties. While spanning …

The Development Of Parallel Adaptive Sampling Algorithms For Analyzing Biological Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Sanjukta Bhowmick, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

The availability of biological data in massive scales continues to represent unlimited opportunities as well as great challenges in bioinformatics research. Developing innovative data mining techniques and efficient parallel computational methods to implement them will be crucial in extracting useful knowledge from this raw unprocessed data, such as in discovering significant cellular subsystems from gene correlation networks. In this paper, we present a scalable combinatorial sampling technique, based on identifying maximum chordal subgraphs, that reduces noise from biological correlation networks, thereby making it possible to find biologically relevant clusters from the filtered network. We show how selecting the appropriate filter …

A Novel Multithreaded Algorithm For Extracting Maximal Chordal Subgraphs, Mahantesh Halappanavar, John Feo, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick

Interdisciplinary Informatics Faculty Proceedings & Presentations

Chordal graphs are triangulated graphs where any cycle larger than three is bisected by a chord. Many combinatorial optimization problems such as computing the size of the maximum clique and the chromatic number are NP-hard on general graphs but have polynomial time solutions on chordal graphs. In this paper, we present a novel multithreaded algorithm to extract a maximal chordal sub graph from a general graph. We develop an iterative approach where each thread can asynchronously update a subset of edges that are dynamically assigned to it per iteration and implement our algorithm on two different multithreaded architectures - Cray …

A Parallel Graph Sampling Algorithm For Analyzing Gene Correlation Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Hesham Ali, Sanjukta Bhowmick

Interdisciplinary Informatics Faculty Publications

Effcient analysis of complex networks is often a challenging task due to its large size and the noise inherent in the system. One popular method of overcoming this problem is through graph sampling, that is extracting a representative subgraph from the larger network. The accuracy of the sample is validated by comparing the combinatorial properties of the subgraph and the original network. However, there has been little study in comparing networks based on the applications that they represent. Furthermore, sampling methods are generally applied agnostically, without mapping to the requirements of the underlying analysis. In this paper,we introduce a parallel …

Evaluation Of Essential Genes In Correlation Networks Using Measures Of Centrality, Kathryn Dempsey Cooper, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Correlation networks are emerging as powerful tools for modeling relationships in high-throughput data such as gene expression. Other types of biological networks, such as protein-protein interaction networks, are popular targets of study in network theory, and previous analysis has revealed that network structures identified using graph theoretic techniques often relate to certain biological functions. Structures such as highly connected nodes and groups of nodes have been found to correspond to essential genes and protein complexes, respectively. The correlation network, which measures the level of co-variation of gene expression levels, shares some structural properties with other types of biological networks. We …

A Novel Correlation Networks Approach For The Identification Of Gene Targets, Kathryn Dempsey Cooper, Stephen Bonasera, Dhundy Raj Bastola, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

Correlation networks are emerging as a powerful tool for modeling temporal mechanisms within the cell. Particularly useful in examining coexpression within microarray data, studies have determined that correlation networks follow a power law degree distribution and thus manifest properties such as the existence of “hub” nodes and semicliques that potentially correspond to critical cellular structures. Difficulty lies in filtering coincidental relationships from causative structures in these large, noise-heavy networks. As such, computational expenses and algorithm availability limit accurate comparison, making it difficult to identify changes between networks. In this vein, we present our work identifying temporal relationships from microarray data …

A Noise Reducing Sampling Approach For Uncovering Critical Properties In Large Scale Biological Networks, Karthik Duraisamy, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick

Interdisciplinary Informatics Faculty Proceedings & Presentations

A correlation network is a graph-based representation of relationships among genes or gene products, such as proteins. The advent of high-throughput bioinformatics has resulted in the generation of volumes of data that require sophisticated in silico models, such as the correlation network, for in-depth analysis. Each element in our network represents expression levels of multiple samples of one gene and an edge connecting two nodes reflects the correlation level between the two corresponding genes in the network according to the Pearson correlation coefficient. Biological networks made in this manner are generally found to adhere to a scale-free structural nature, that …

Appearance Based Stage Recognition Of Drosophila Embryos, Gopi Chand Nutakki

Masters Theses & Specialist Projects

Stages in Drosophila development denote the time after fertilization at which certain specific events occur in the developmental cycle. Stage information of a host embryo, as well as spatial information of a gene expression region is indispensable input for the discovery of the pattern of gene-gene interaction. Manual labeling of stages is becoming a bottleneck under the circumstance of high throughput embryo images. Automatic recognition based on the appearances of embryos is becoming a more desirable scheme. This problem, however, is very challenging due to severe variations of illumination and gene expressions. In this research thesis, we propose an appearance …

An Intelligent Data-Centric Approach Toward Identification Of Conserved Motifs In Protein Sequences, Kathryn Dempsey Cooper, Benjamin Currall, Richard Hallworth, Hesham Ali

Interdisciplinary Informatics Faculty Proceedings & Presentations

The continued integration of the computational and biological sciences has revolutionized genomic and proteomic studies. However, efficient collaboration between these fields requires the creation of shared standards. A common problem arises when biological input does not properly fit the expectations of the algorithm, which can result in misinterpretation of the output. This potential confounding of input/output is a drawback especially when regarding motif finding software. Here we propose a method for improving output by selecting input based upon evolutionary distance, domain architecture, and known function. This method improved detection of both known and unknown motifs in two separate case studies. …

Mcm-Test: A Fuzzy-Set-Theory-Based Approach To Differential Analysis Of Gene Pathways, Lily R. Liang, Vinay Mandal, Yi Lu, Deepak Kumar

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Gene pathway can be defined as a group of genes that interact with each other to perform some biological processes. Along with the efforts to identify the individual genes that play vital roles in a particular disease, there is a growing interest in identifying the roles of gene pathways in such diseases.

Results

This paper proposes an innovative fuzzy-set-theory-based approach, Multi-dimensional Cluster Misclassification test (MCM-test), to measure the significance of gene pathways in a particular disease. Experiments have been conducted on both synthetic data and real world data. Results on published diabetes gene expression dataset and a list …

Fm-Test: A Fuzzy-Set-Theory-Based Approach To Differential Gene Expression Data Analysis, Lily R. Liang, Shiyong Lu, Xuena Wang, Yi Lu, Vinay Mandal, Dorrelyn Patacsil, Deepak Kumar

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

Microarray techniques have revolutionized genomic research by making it possible to monitor the expression of thousands of genes in parallel. As the amount of microarray data being produced is increasing at an exponential rate, there is a great demand for efficient and effective expression data analysis tools. Comparison of gene expression profiles of patients against those of normal counterpart people will enhance our understanding of a disease and identify leads for therapeutic intervention.

Results

In this paper, we propose an innovative approach, fuzzy membership test (FM-test), based on fuzzy set theory to identify disease associated genes from microarray …

Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai

Research Collection School Of Computing and Information Systems

With the huge amount of data collected by scientists in the molecular genetics community in recent years, there exists a need to develop some novel algorithms based on existing data mining techniques to discover useful information from genome databases. We propose an algorithm that integrates the statistical method, association rule mining, and classification rule mining in the discovery of allelic combinations of genes that are peculiar to certain phenotypes of diseased patients.

Images 2.1 : An Integrated Model Of An Arid Grazing System, Z G. Yan, K M. Wang

Resource management technical reports

The name IMAGES stands for An Integrated Model of an Arid Grazing Ecological System. The model was initiated by Dr R. Hacker in 1987-8 as an activity to (1) evaluate alternative management strategies and (2) identify key ecological processes and research priorities in shrub rangelands of Western Australia. Version 1 of the model was published in Agriculture Systems in 1991 (Hacker et al. 1991) and here after will be referred to as IMAGES 1. IMAGES 1 is a vegetation model, capable of predicting the prob ility of recruitment and mortality of the desirable species in a given vegetation type under …