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Machine Learning Based Drug-Disease Relationship Prediction and Characterization

Yaddanapudi, Suryanarayana
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565349706029458

Abstract Details

2019, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Drug repurposing is the process of finding novel uses for approved or failed drugs. Recently, several computational approaches coupled with big-data analytics are making it possible to systematically and rapidly evaluate the repurposing opportunities in an automated fashion. While some approaches focus on matching drug and disease gene expression profiles, the others rely on the interaction between protein targets and integrated mechanistic relationships from molecular to system level into computational drug repurposing candidate discovery platforms. The work done for my thesis aims at characterizing the drug-disease associations using computational framework through drug related side-effects, shared phenotypes and gene feature annotations. In this regard, I proposed three approaches. In the first approach, I built a drug-drug interactome based on side-effects data and predicted drug-disease interactions using graph-based clustering algorithms (Chapter 3). In the second approach called PhenoRx, I ranked drug-disease associations based on shared phenotypes using cosine similarity integrated with term frequency–inverse document frequency and identify novel drug-disease relationships (Chapter 4). Finally, in the third approach called FeatuRx, I matched drugs and diseases based on the incidence of shared features like pathways, biological processes, and phenotypes and implemented machine learning classifiers to discover potentially novel drug-disease associations (Chapter 5). I validated the three machine learning-based approaches using different performance metrics and the results obtained suggest that these approaches can be useful in drug discovery, drug repurposing, and pharmacovigilance studies.
Anil Jegga, D.V.M. (Committee Chair)
Sam Anand, Ph.D. (Committee Member)
Samuel Huang, Ph.D. (Committee Member)
Mayur Sarangdhar, PhD (Committee Member)
David Thompson, Ph.D. (Committee Member)
102 p.
Bioinformatics
Machine Learning; Drug Repurposing; Drug Repositioning; Natural language Processing; Graph Clustering

Recommended Citations

Citations

  • Yaddanapudi, S. (2019). Machine Learning Based Drug-Disease Relationship Prediction and Characterization [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565349706029458

    APA Style (7th edition)

  • Yaddanapudi, Suryanarayana. Machine Learning Based Drug-Disease Relationship Prediction and Characterization. 2019. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565349706029458.

    MLA Style (8th edition)

  • Yaddanapudi, Suryanarayana. "Machine Learning Based Drug-Disease Relationship Prediction and Characterization." Doctoral dissertation, University of Cincinnati, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565349706029458

    Chicago Manual of Style (17th edition)

ucin1565349706029458
138
© 2019, some rights reserved.Creative Commons License Machine Learning Based Drug-Disease Relationship Prediction and Characterization by Suryanarayana Yaddanapudi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.