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- Chordal graphs (5)
- Correlation networks (5)
- Clustering (2)
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- Bioinformatics (1)
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Articles 1 - 15 of 15
Full-Text Articles in Physical Sciences and Mathematics
Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson
Data Analytics Pipeline For Rna Structure Analysis Via Shape, Quinn Nelson
UNO Student Research and Creative Activity Fair
Coxsackievirus B3 (CVB3) is a cardiovirulent enterovirus from the family Picornaviridae. The RNA genome houses an internal ribosome entry site (IRES) in the 5’ untranslated region (5’UTR) that enables cap-independent translation. Ample evidence suggests that the structure of the 5’UTR is a critical element for virulence. We probe RNA structure in solution using base-specific modifying agents such as dimethyl sulfate as well as backbone targeting agents such as N-methylisatoic anhydride used in Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension (SHAPE). We have developed a pipeline that merges and evaluates base-specific and SHAPE data together with statistical analyses that provides confidence …
Designing Novel Nanostructured Permanent Magnets, Ali Al Kadhim
Designing Novel Nanostructured Permanent Magnets, Ali Al Kadhim
UNO Student Research and Creative Activity Fair
Rare earth element based alloys have been the source of high performance magnetic alloys, and have played a paramount role in the development of various technologies, including: memory devices (such as credit cards, random-access memory), sensors, and various biomedical applications. However, there is a tremendous need to replace rare earth metals with material with powerful magnetic properties. Our group recently found CrTe-based materials that show very promising magnetic properties in nanostructured form. The magnetic modeling of such material in nanostructured form prior to their fabrication demonstrates their magnetic properties in bulk form. In this project, we investigate the behavior of …
Optimizing Campus Mobility With A Focus On Sustainability: A Graph Theory Approach To Intra-Campus Transportation Networks, Quinn M. Nelson
Optimizing Campus Mobility With A Focus On Sustainability: A Graph Theory Approach To Intra-Campus Transportation Networks, Quinn M. Nelson
UNO Student Research and Creative Activity Fair
The idea of public transportation is supported by most in theory but often heavily criticized by users when put into application. There are common tensions that are related to public transportation, as described by frequent users: unreliable, too crowded, and slow. The University of Nebraska-Omaha (UNO) is a growing metropolitan institution that uses a shuttle system to transport students among their three campuses daily. As of 2015, the current total student enrollment is approximately 16,000; UNO plans to enroll 20,000 students by 2020. The expected student growth is also reflected by the current construction of new buildings and expansion of …
Identifying Aging-Related Genes In Mouse Hippocampus Using Gateway Nodes, Kathryn Dempsey Cooper, Hesham Ali
Identifying Aging-Related Genes In Mouse Hippocampus Using Gateway Nodes, Kathryn Dempsey Cooper, Hesham Ali
Interdisciplinary Informatics Faculty Publications
Background: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph …
A Parallel Template For Implementing Filters For Biological Correlation Networks, Kathryn Dempsey Cooper, Vladimir Ufimtsev, Sanjukta Bhowmick, Hesham Ali
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 Mining Biological Signals Using Correlation Networks, Kathryn Dempsey Cooper, Ishwor Thapa, Claudia Cortes, Zack Eriksen, Dhundy Raj Bastola, Hesham Ali
On Mining Biological Signals Using Correlation Networks, Kathryn Dempsey Cooper, Ishwor Thapa, Claudia Cortes, Zack Eriksen, Dhundy Raj Bastola, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Correlation networks have been used in biological networks to analyze and model high-throughput biological data, such as gene expression from microarray or RNA-seq assays. Typically in biological network modeling, structures can be mined from these networks that represent biological functions; for example, a cluster of proteins in an interactome can represent a protein complex. In correlation networks built from high-throughput gene expression data, it has often been speculated or even assumed that clusters represent sets of genes that are coregulated. This research aims to validate this concept using network systems biology and data mining by identification of correlation network clusters …
On Identifying And Analyzing Significant Nodes In Protein-Protein Interaction Networks, Rohan Khazanchi, Kathryn Dempsey Cooper, Ishwor Thapa, Hesham Ali
On Identifying And Analyzing Significant Nodes In Protein-Protein Interaction Networks, Rohan Khazanchi, Kathryn Dempsey Cooper, Ishwor Thapa, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Network theory has been used for modeling biological data as well as social networks, transportation logistics, business transcripts, and many other types of data sets. Identifying important features/parts of these networks for a multitude of applications is becoming increasingly significant as the need for big data analysis techniques grows. When analyzing a network of protein-protein interactions (PPIs), identifying nodes of significant importance can direct the user toward biologically relevant network features. In this work, we propose that a node of structural importance in a network model can correspond to a biologically vital or significant property. This relationship between topological and …
A Novel Multithreaded Algorithm For Extracting Maximal Chordal Subgraphs, Mahantesh Halappanavar, John Feo, Kathryn Dempsey Cooper, Hesham Ali, Sanjukta Bhowmick
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 …
On The Design Of Advanced Filters For Biological Networks Using Graph Theoretic Properties, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sanjukta Bhowmick, Hesham Ali
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
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 Parallel Graph Sampling Algorithm For Analyzing Gene Correlation Networks, Kathryn Dempsey Cooper, Kanimathi Duraisamy, Hesham Ali, Sanjukta Bhowmick
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
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
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
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 …
An Intelligent Data-Centric Approach Toward Identification Of Conserved Motifs In Protein Sequences, Kathryn Dempsey Cooper, Benjamin Currall, Richard Hallworth, Hesham Ali
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. …