Browsing by Subject "Computer Science"

Now showing 1 - 3 of 3
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    GRAPH BASED MINING ON WEIGHTED DIRECTED GRAPHS FOR SUBNETWORKS AND PATH DISCOVERY
    (2011-08-16) Abdulkarim, Sijin Cherupilly; Palakal, Mathew J.; Fang, Shiaofen; Xia, Yuni
    Subnetwork or path mining is an emerging data mining problem in many areas including scientific and commercial applications. Graph modeling is one of the effective ways in representing real world networks. Many natural and man-made systems are structured in the form of networks. Traditional machine learning and data mining approaches assume data as a collection of homogenous objects that are independent of each other whereas network data are potentially heterogeneous and interlinked. In this paper we propose a novel algorithm to find subnetworks and Maximal paths from a weighted, directed network represented as a graph. The main objective of this study is to find meaningful Maximal paths from a given network based on three key parameters: node weight, edge weight, and direction. This algorithm is an effective way to extract Maximal paths from a network modeled based on a user’s interest. Also, the proposed algorithm allows the user to incorporate weights to the nodes and edges of a biological network. The performance of the proposed technique was tested using a Colorectal Cancer biological network. The subnetworks and paths obtained through our network mining algorithm from the biological network were scored based on their biological significance. The subnetworks and Maximal paths derived were verified using MetacoreTM as well as literature. The algorithm is developed into a tool where the user can input the node list and the edge list. The tool can also find out the upstream and downstream of a given entity (genes/proteins etc.) from the derived Maximal paths. The complexity of finding the algorithm is found to be O(nlogn) in the best case and O(n^2 logn) in the worst case.
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    The IUPUI Signature Center on Bio-Computing
    (Office of the Vice Chancellor for Research, 2010-04-09) Fang, Shiaofen; Mukhopadhyay, Snehasis
    Bio-Computing is the discipline that integrates biomedical concepts and Computer Science techniques for collecting, managing, processing and analyzing large-scale biomedical data, as well as enables a deeper understanding of biological processes and medical procedures through modeling, simulation, and visualization. Bio-Computing emphasizes the algorithmic, computational, and software system issues arising from biomedical problems. It focuses on developing new, improved, specialized and customized Computer Science techniques and tools for computing related needs in life science applications that do not have ready-to-use solutions. The IUPUI Signature Center on Bio-Computing (SCBC) aims to act as a catalyst to provide BioComputing infrastructure and expertise for Indiana life science initiative. The specific mission is the following: • Bio-Computing Infrastructure: To develop cutting-edge bio-computing techniques and tools to establish an infrastructure as a framework to support life science applications. • Collaborative Projects: To actively engage in collaborative research projects, and maximize the impact of bio-computing in life science research and funding efforts. The scope of the projects supported by SCBC can be best described by the figure below:
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    Text Mining for Social Harm and Criminal Justice Applications
    (2020-08) Pandey, Ritika; Mohler, George; Hasan, Mohammad Al; Mukhopadhyay, Snehasis
    Increasing rates of social harm events and plethora of text data demands the need of employing text mining techniques not only to better understand their causes but also to develop optimal prevention strategies. In this work, we study three social harm issues: crime topic models, transitions into drug addiction and homicide investigation chronologies. Topic modeling for the categorization and analysis of crime report text allows for more nuanced categories of crime compared to official UCR categorizations. This study has important implications in hotspot policing. We investigate the extent to which topic models that improve coherence lead to higher levels of crime concentration. We further explore the transitions into drug addiction using Reddit data. We proposed a prediction model to classify the users’ transition from casual drug discussion forum to recovery drug discussion forum and the likelihood of such transitions. Through this study we offer insights into modern drug culture and provide tools with potential applications in combating opioid crises. Lastly, we present a knowledge graph based framework for homicide investigation chronologies that may aid investigators in analyzing homicide case data and also allow for post hoc analysis of key features that determine whether a homicide is ultimately solved. For this purpose we perform named entity recognition to determine witnesses, detectives and suspects from chronology, use keyword expansion to identify various evidence types and finally link these entities and evidence to construct a homicide investigation knowledge graph. We compare the performance over several choice of methodologies for these sub-tasks and analyze the association between network statistics of knowledge graph and homicide solvability.