Leveraging knowledge networks for precision medicine

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Wang, Sheng

Contributors dc:contributor

• Peng, Jian
• Zhai, ChengXiang
• Han, Jiawei
• Sinha, Saurabh
• Lu, Xinghua

• Knowledge networks, precision medicine

Statement dc:rights

• Copyright 2018, Sheng Wang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/101189

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/101189