Resume

• 1985

  Ph.D.

  Computer Science

  University of Nebraska-Lincoln

  University of Nebraska at Omaha

• Pattern Recognition

  Signal Processing

  Bioinformatics

  Programming

  Computer Science

  Systems Biology

  Perl

  Distributed Systems

  Wireless Networking

  C++

  Artificial Intelligence

  Data Mining

  R

  Wireless Sensor Networks

  Python

  Computational Biology

  Java

  Algorithms

  Statistics

  Machine Learning

  An energy-aware genetic algorithm for managing self-organized wireless sensor networks

  Majority of the current Wireless Sensor Networks (WSNs) research have prioritized either the coverage of the monitored area or the energy efficiency of the network. We have focused on attaining a solution that considers several optimization parameters such as the percentage of coverage

  quality of coverage and energy consumption. The problem is modeled using a bipartite graph and employs an evolutionary algorithm to handle the activation and deactivation of the sensors.

  An energy-aware genetic algorithm for managing self-organized wireless sensor networks

  Exploring Database Keyword Search for Association Studies between Genetic Variants and Diseases

  Keyword search plays a critical role for researchers in bioinformatics to retrieve structured

  semi-structured

  and unstructured data. In addition

  in order to fully exploit the rich repository of biological databases

  data mining has drawn increasing attention of researchers. An interesting issue is to examine the possible relationship between database keyword search (DB KWS) and in- depth database exploration (or data mining) in the context of bioinformatics

  and in particular

  the potential contribution of DB KWS for data mining.In this paper

  we provide a preliminary discussion on how we can take advantage of DB KWS for in-depth exploration of biological databases

  and describe a case study on the association between genetic variants and diseases.

  Exploring Database Keyword Search for Association Studies between Genetic Variants and Diseases

  Sanjukta Bhowmick

  T.-Y. Chen

  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. \nPrevious 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 filter that incorporates multiple graph theoretic concepts can improve upon network \nfiltering. We use three different clustering methods to highlight the ability of the filter to maintain network clusters

  and find evidence that suggests the clusters maintained are of high importance in the original unfiltered network due to high-degree and biological relevance (essentiality). Our filter highlights the advantages of integration of graph theoretic concepts into biological network analysis. \n

  A structure-preserving hybrid filter for sampling in correlation networks

  Hesham

  Ali

  University of Nebraska at Omaha

  University of Nebraska at Omaha

  Professor of Computer Science

  University of Nebraska at Omaha

  Dean - College of Information Science and Technology

  University of Nebraska at Omaha