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- Chordal graphs (4)
- Correlation networks (4)
- Graph theory (3)
- Bioinformatics (2)
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- Clustering (2)
- Edge enrichment (2)
- Motif finding (2)
- Network filters (2)
- Ontology (2)
- Biological properties (1)
- Biology computing (1)
- Biomedicine (1)
- Centrality (1)
- Cluster overlap (1)
- Clusters (1)
- Convergence (1)
- Correlation network (1)
- Data handling (1)
- Driver nodes (1)
- Drug discovery (1)
- Essential genes (1)
- Gene expression (1)
- Genetics (1)
- Hub nodes (1)
- Hub nodes; spanning trees (1)
- Hubs (1)
- Intelligent tools (1)
- Large scale networks (1)
- Lethal genes (1)
Articles 1 - 14 of 14
Full-Text Articles in Life Sciences
A Novel Approach To Identify Shared Fragments In Drugs And Natural Products, Ashkay Balasubramanya, Ishwor Thapa, Dhundy Raj Bastola, Dario Ghersi
A Novel Approach To Identify Shared Fragments In Drugs And Natural Products, Ashkay Balasubramanya, Ishwor Thapa, Dhundy Raj Bastola, Dario Ghersi
Interdisciplinary Informatics Faculty Proceedings & Presentations
Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are …
On The Comparison Of State- And Transition-Based Analysis Of Biological Relevance In Gene Co-Expression Networks, Kathryn Dempsey Cooper, Prasuna Vemuri, Hesham Ali
On The Comparison Of State- And Transition-Based Analysis Of Biological Relevance In Gene Co-Expression Networks, Kathryn Dempsey Cooper, Prasuna Vemuri, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Traditional correlation network analysis typically involves creating a network using gene expression data and then identifying biologically relevant clusters from that network by enrichment with Gene Ontology or pathway information. When one wants to examine these networks in a dynamic way - such as between controls versus treatment or over time - a "snapshot" approach is taken by comparing network structures at each time point. The biological relevance of these structures are then reported and compared. In this research, we examine the same "snapshot" networks but focus on the enrichment of changes in structure to determine if these results give …
Identifying Pathway Proteins In Networks Using Convergence, Kathryn Dempsey Cooper, Hesham Ali
Identifying Pathway Proteins In Networks Using Convergence, Kathryn Dempsey Cooper, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
One of the key goals of systems biology concerns the analysis of experimental biological data available to the scientific public. New technologies are rapidly developed to observe and report whole-scale biological phenomena; however, few methods exist with the ability to produce specific, testable hypotheses from this noisy ‘big’ data. In this work, we propose an approach that combines the power of data-driven network theory along with knowledge-based ontology to tackle this problem. Network models are especially powerful due to their ability to display elements of interest and their relationships as internetwork structures. Additionally, ontological data actually supplements the confidence of …
A Structure-Preserving Hybrid-Chordal Filter For Sampling In Correlation Networksa Structure-Preserving Hybrid-Chordal Filter For Sampling In Correlation Networks, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sriram Srinivasan, Sanjukta Bhowmick, Hesham Ali
A Structure-Preserving Hybrid-Chordal Filter For Sampling In Correlation Networksa Structure-Preserving Hybrid-Chordal Filter For Sampling In Correlation Networks, Kathryn Dempsey Cooper, Tzu-Yi Chen, Sriram Srinivasan, Sanjukta Bhowmick, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Biological networks are fast becoming a popular tool for modeling high-throughput data, especially due to the ability of the network model to readily identify structures with biological function. However, many networks are fraught with noise or coincidental edges, resulting in signal corruption. Previous work has found that the implementation of network filters can reduce network noise and size while revealing significant network structures, even enhancing the ability to identify these structures by exaggerating their inherent qualities. In this study, we implement a hybrid network filter that combines features from a spanning tree and near-chordal subgraph identification to show how a …
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 …
Identifying Modular Function Via Edge Annotation In Gene Correlation Networks Using Gene Ontology Search, Kathryn Dempsey Cooper, Ishwor Thapa, Dhundy Raj Bastola, Hesham Ali
Identifying Modular Function Via Edge Annotation In Gene Correlation Networks Using Gene Ontology Search, Kathryn Dempsey Cooper, Ishwor Thapa, Dhundy Raj Bastola, Hesham Ali
Interdisciplinary Informatics Faculty Proceedings & Presentations
Correlation networks provide a powerful tool for analyzing large sets of biological information. This method of high-throughput data modeling has important implications in uncovering novel knowledge of cellular function. Previous studies on other types of network modeling (protein-protein interaction networks, metabolomes, etc.) have demonstrated the presence of relationships between network structures and organization of cellular function. Studies with correlation network further confirm the existence of such network structure and biological function relationship. However, correlation networks are typically noisy and the identified network structures, such as clusters, must be further investigated to verify actual cellular function. This is traditionally done using …
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. …