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Research Interests

  • Pattern Recognition and Data mining
  • Machine Learning and Numerical analysis
  • Genome sequencing analysis
  • Visualized Medical Data Analysis

Current Research Projects

  • Network Motif Analysis
  • Evolutionary dynamics through genome sequencing analysis
  • VIA-QMI: A software program for immunotherapy research


Figure 1: Biological Networks

Network Motif Analysis

Network motif is a frequent and unique subgraph pattern that can be found in biological networks shown in the left. Network motifs have been applied to find breast-cancer related genes, predict essential proteins, and generate protein interactions. Methods to detect network motifs are categorized into network-based and motif-based approaches. Network-based aproach searches all possible patterns from the input graph, while motif-based approach finds the instances of given query patterns. Fanmod program implements network-based, and Moda implements motif-based approach.


Figure 2: Network Motif Detection

Examples of network motifs are shown in the left. Out of all non-isomorphic size-4 graph patterns, shown in the upper right, the method can identify frequent and unique patterns (three patterns as in the bottom), and determine those are the network motifs.
Network motif detection process is computationally expensive, and most of programs provide the patterns only without related their instances. We defined a new network motif representation called NemoProfile, which can map pattern to their instances.

We developed network motif libraries (NemoLib) in C++, Java and Pythond, that have NemoProfile functionality. Also we improved the performance of MODA program, which is named as NemoMap. Students have developed web-based network motif detection program using NemoLib.

NemoSuite: Web-based Network Motif Analytic Suite

Web-QuateXelero: Web-based efficient network motif detection tool

Download NemoLib: C++, Java, Python

Download NemoMap

Example network file: smallnetwork, bignetwork



Figure 3: Web-based Database System

Evolutionary dynamics through genome sequencing analysis

We are collaborating with Dr. Kristina Hillesland on Investigating evolution of a microbial mutualism. Sequencing DNA results in many small chunks of DNA strands, which are called reads that combine to make the whole genome. In general, reconstructing the whole DNA sequence from these reads is done in a series of processes including genome alignment, variant calling, and annotation.

We got initial results on list of mutations by the Breseq program. They are available as mutation tables and in a database with muller diagrams (password-protected for now). We are running mutliple tools to detect reliable mutations, and developing an algorithm for detecting structural variants.

VIA-QMI: A software program for immunotherapy research

We are collaborating with Dr. Stephen Smith on Immonotherapy research. We are developing VIA-QMI (Visualized data analytic tool for QMI), which is a software program that facilitates the Quantitative Multiplex Co-Immunoprecipitation (QMI) platform. The QMI platform generates high-dimentional data that describe the protein-protein interaction network state of the cell. To maximize the usability, and increase data analytic visualization of the process, VIA-QMI is a graphical user interface program that provides a streamlined workflow and an intuitive and interactive interface.


Figure 4: Description of QMI assay


  • Co-PI, Quantitative protein network profiling to improve car design and efficacy, (PI: Dr. Stephen Smith, SEATTLE CHILDREN'S HOSPITAL), National Institutes of Health, support for 5 years:2020-2025, $600,156
  • Research Assistant Support, Computing and Software Systems Division, University of Washington
    • Patterns in a graph to detect structural variants from genome sequencing reads, support for three quarters 2019-2020
    • Graphical User Interface Program for Quantitative Multiplex Co-Immunoprecipitation, support for two quarters 2018-2019
    • Network Motif in Cloud, support for three quarters 2017-2018
    • Reconstruction of Evolutionary Biological Networks, support for two quarters 2016-2017
  • Graduate Research Program, Computing and Software Systems Division, University of Washington
    • Pattern-based clustering approach to detecting structural variants from next-generation sequencing data, Winter, Spring, and Summer 2019 ($5,400)
    • Trace mutations in co-evolved organisms with a web-interactive web database system, support for one quarter, Winter 2018 ($3,500)
    • Reconstruction of evolutionary PPI networks, Autumn 2016($5,500), Winter 2017 ($5,500), Spring 2017 ($5,500)
    • Machine Learning in Genomics over clouds, Autumn 2014 ($4,400)
    • Motif discovery in MapReduce, Winter 2014 ($2,196)
  • PI, Coevolution in-action: Beyond Next Generation Sequencing (NGS) Analysis in Cloud Computing Environments, Microsoft Azure Award, support for one year, 2015, $20,000
  • PI, Coevolution in-action: Next Generation Data Sequencing Analysis in Cloud Computing Platforms, Google Cloud Credit, support for one year: Jul 15, 2014 - Jul 14, 2015, $10,000
  • Co-PI, RUI: Characterization of Coevolution, Adaptation, and Diversification in a Model Microbial Mutualism, (PI: Dr. Kristina Hillesland), National Science Foundation, Division of Environmental Biology, support for 3 years:2013-2016, $400,000

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