Browsing by Author "Alam, Maksudul"

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    Large Scale Network Visualization with Gephi
    Alam, Maksudul; Arifuzzaman, S. M.; Bhuiyan, Md Hasanuzzaman (2012-12-11)
    The notion of graphs or networks is sufficiently pervasive since it can be used to model various types of data sources. Social, biological, and other networks capture the underlying structural and relational properties. Analysis of different networks reveals interesting information of the corresponding domain or system. Network analysts, thus, strive to analyze various networks by applying different algorithms and try to connect obtained insights to make sense of a unified theme, pattern or structure. For example, analysis of facebook friend network of a person can reveal information such as, groups of highly clustered people, most influential person in terms of connections, connecting persons between different cluster of people, etc. While analyzing networks and digesting the information therein, analysts gradually form internal mental models of the people, places, events, or any sort of entity represented in the networks. As the number of nodes grows larger, however, it becomes increasingly difficult for an investigator to track the connections between data and make sense of it all. Many researchers believe that visual representations of data can help people better examine, analyze, and understand them. Norman [Norman94] has described how visual representations can help augment people’s thinking and analysis processes. The objective of the project is to develop visual representations of nodes, edges, and labels of a network in order to help analysts search, review, and understand the network better. We seek to create interactive visualizations that will highlight and identify significance of nodes, cluster formation, etc., in the networks where entities may be, for example, people, places, webpage, biological entity, dates and organizations. Basically, we want to build visual representations of the networks that help analysts making sense by applying different algorithms on them and observe the difference of nodes and edges in terms of color, and size. A very important aspect of the project is the integration of the visualization module with CINET [CINET2012], a cyberinfrastructure for network science. CINET includes a set of graph algorithms and various types of networks. Analysis of networks are done by applying algorithms on those networks; results are obtained as text files containing information of different measures of nodes or edges. Complex workflow is intended while working with CINET where output of one analysis can be used as input to further analysis. Visualization comes as a great aid when analyst want to filter his interest on some particular nodes or a portion of the graph and conduct subsequent analysis on the smaller part. Though there are some existing visualization tools, e.g., Jigsaw [Jigsaw08], Sentinel Visualizer, NetLens, etc., they are more focused on information representation rather than on graph exploration or summarization capabilities. To the best of our knowledge, our project is the only one which supports network visualization as a part of complex workflow within network analysis utilizing high performance computing environment. In summary, this project develops a visualization component for a VT digital library containing large network graphs (e.g., social networks and transportation networks). The visualization service will get datasets from an existing DL, visualize the graphs using Gephi (a java-based visualization library), and integrate the results within an NSF supported cyberinfrastructure (CINET).
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    Predictive Computational Modeling of the Mucosal Immune Responses during Helicobacter pylori Infection
    Carbo, Adria; Bassaganya-Riera, Josep; Pedragosa, Mireia; Viladomiu, Monica; Marathe, Madhav; Eubank, Stephen; Wendesdorf, Katherine; Bisset, Keith R.; Hoops, Stefan; Deng, Xinwei; Alam, Maksudul; Kronsteiner, Barbara; Mei, Yongguo; Hontecillas, Raquel (Public Library of Science, 2013-09-05)
    T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levelｙs of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor γ (PPARγ) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPARγ knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPARγ during H. pylori infection affecting disease outcomes.
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    Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection
    Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith R.; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav (PLOS, 2015-09-01)
    Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.
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    Text Classification Using Mahout
    Alam, Maksudul; Arifuzzaman, S. M.; Bhuiyan, Md Hasanuzzaman (2012-11-06)
    This module focuses on classification of text using Apache Mahout. After successful completion of this module, students will be able to explain and apply methods of classification, correctly classify a set of documents using Apache Mahout, and construct and apply workflows for text classification using Apache Mahout.