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Dimension Reduction for Network Analysis with an Application to Drug Discovery

Chen, Huiyuan
http://rave.ohiolink.edu/etdc/view?acc_num=case1598303654048332

Abstract Details

2020, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
Graphs (or networks) naturally represent valuable information for relational data, which are ubiquitous in real-world applications, such as social networks, recommender systems, and biological networks. Statistical learning or machine learning techniques for network analysis, such as random walk with restart, meta-path analysis, network embeddings, and matrix/tensor factorizations, have gained tremendous attentions recently. With rapid growth of data, networks, either homogeneous or heterogeneous, can consist of billions of nodes and edges. How can we find underlying structures within a network? How can we efficiently manage data when multiple sources describing the networks are available? How can we detect the most important relationships among nodes? To gain insights into these problems, this dissertation investigates the principles and methodologies of dimension reduction techniques that explore the useful latent structures of one or more networks. Our dimension reduction techniques mainly leverage recent developments in linear algebra, graph theory, large-scale optimization, and deep learning. In addition, we also translate our ideas and models to several real-world applications, especially in drug repositioning, drug combinations, and drug-target-disease interactions. For each research problem, we discuss their current challenges, related work, and propose corresponding solutions.
Jing Li, Dr. (Committee Chair)
Harold Connamacher, Dr. (Committee Member)
Xusheng Xiao, Dr. (Committee Member)
Satya Sahoo, Dr. (Committee Member)
Computer Science
Machine Learning, Network Embedding, Drug Discovery, Deep Learning

Recommended Citations

Citations

  • Chen, H. (2020). Dimension Reduction for Network Analysis with an Application to Drug Discovery [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1598303654048332

    APA Style (7th edition)

  • Chen, Huiyuan. Dimension Reduction for Network Analysis with an Application to Drug Discovery. 2020. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1598303654048332.

    MLA Style (8th edition)

  • Chen, Huiyuan. "Dimension Reduction for Network Analysis with an Application to Drug Discovery." Doctoral dissertation, Case Western Reserve University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1598303654048332

    Chicago Manual of Style (17th edition)

case1598303654048332
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This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.