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Title:Leveraging knowledge networks for precision medicine
Author(s):Wang, Sheng
Director of Research:Peng, Jian
Doctoral Committee Chair(s):Zhai, ChengXiang
Doctoral Committee Member(s):Han, Jiawei; Sinha, Saurabh; Lu, Xinghua
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Knowledge networks, precision medicine
Abstract:Akin to the exponential growth of genomic sequencing data, high-throughput techniques in proteomics and biotechnology have been creating ever-expanding repositories of proteomic, pharmacological, and interactomic data. Other molecular data, including expression profiles, genomic mutations and cell conditions, have also been massively generated and they are further refining our understanding of disease mechanisms. In addition, patient data, gathered by electronic medical record systems and social medias, complement biological data and pave the way for personalized treatment strategies. Therefore, efficiently and effectively integrating and mining these invaluable data hold the great promising of making precision medicine a reality. However, integrating and mining these large-scale, heterogeneous, and noisy dataset pose several fundamental computational challenges and have therefore become a bottleneck to clinical decision making and medical knowledge discovery. This thesis is a systematic study of mining these biological and healthcare data for precision medicine. I take a network perspective and integrate these datasets into a large knowledge network where nodes are biological concepts and links are biological relationships. I then propose a novel computational framework to mine these knowledge networks. To demonstrate the effectiveness of mining knowledge networks, I will introduce how this framework can be used to understand molecular functions, accelerate drug discovery, and support clinical decision making. To understand molecular functions, I will show how a knowledge network can substantially improve gene function prediction performance and further annotate novel gene sets by mining scientific literature-based knowledge network. To accelerate drug discovery, I will use the knowledge network to predict drug targets and identify drug associated pathways. To support clinical decision making, I will discuss our efforts in integrating genomics data with clinical data to cluster patients, predict patient survival and visualize patient records. Finally, I will conclude this thesis by summarizing how mining knowledge networks advance precision medicine and discussing the promising future work of this thesis.
Issue Date:2018-04-18
Type:Text
URI:http://hdl.handle.net/2142/101189
Rights Information:Copyright 2018, Sheng Wang
Date Available in IDEALS:2018-09-04
2020-09-05
Date Deposited:2018-05


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