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Big Data Approaches to Improving the Identification of Drug or Disease Mechanisms for Drug Innovation

Wang, Muying (2020) Big Data Approaches to Improving the Identification of Drug or Disease Mechanisms for Drug Innovation. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Advances in science and technology have substantially changed drug research and development (R&D) processes. However, the efficiency of drug R&D, described in the number of new drugs approved per billion US dollars spent, dramatically declined between 1950 to 2010. Some of the main causes to the attrition include the cautious regulator, the potential risk in chemical screening methods for early drug discovery, and the lack of understanding
of disease mechanisms. In order to improve the efficiency and productivity in drug R&D, more powerful tools are needed to assist in prediction forecasting and decision making in drug development. This dissertation describes my work in developing computational approaches to provide
better understanding of drug or disease mechanisms at the systems level. The first project involves collaboration with RIKEN institute in Japan for innovation of influenza vaccine adjuvant. We performed comparative analysis of RNA-Seq data from mice treated with different adjuvants to identify mechanisms supporting adjuvant activity. In the second project, we predicted immune cell dynamics by linear regression-based algorithms or statistical tools, and suggested a new approach that can improve the discovery of key disease-associated genes. In the third project, we found that the network topological features, especially network betweenness, predominantly define the accuracy of a major drug target inference algorithm. We proposed a novel algorithm, TREAP, which integrated betweenness and differential gene expression and can accurately predict drug targets in a time-efficient manner. Through the projects above, we have demonstrated how computational algorithms can assist in mining big biological data to improve understanding of drug or disease mechanisms for drug innovation and development.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Wang, Muyingmuw2@pitt.edumuw2
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorShoemaker, Jasonjason.shoemaker@pitt.edu
Committee MemberBanerjee, Ipsitaipb1@pitt.edu
Committee MemberFaeder, Jamesfaeder@pitt.edu
Committee MemberWilmer, Christopherwilmer@pitt.edu
Date: 30 July 2020
Date Type: Publication
Defense Date: 27 February 2020
Approval Date: 30 July 2020
Submission Date: 25 March 2020
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 114
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Disease mechanism, drug target, network topology, gene expression, immune cell quantity, deconvolution algorithm, influenza vaccine adjuvant
Date Deposited: 30 Jul 2020 19:12
Last Modified: 30 Jul 2020 19:12
URI: http://d-scholarship.pitt.edu/id/eprint/38385

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