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Data-driven Approaches to Understand Development, Diseases and Identify Therapeutics

Wang, Yunguan
http://orcid.org/0000-0001-8489-4504
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535704902199176

Abstract Details

2018, PhD, University of Cincinnati, Medicine: Pathobiology and Molecular Medicine.
Recent technological advances in biomedical, genomics, and computational fields have brought exponential growth in both the amount and accessibility of biological data. These include health records, medical imaging data, omics data including genomics, proteomics, metabolomics, phenomics, disease, and small molecule data. The resultant biological big data poses both great opportunities and challenges. For example, the large amount of heterogeneous data not only allows researchers to query and pursue investigations in health and disease from an unprecedented wide perspective but also enables novel discoveries that were previously obscured by the lack of comprehensive and in-depth analysis. In this dissertation, I use various data-driven approaches to generate testable hypotheses and actionable biological insights related to lung development, disease, and candidate therapeutic discovery. In the first part of this thesis, I used unsupervised machine learning to identify novel cell type and sub types in developing mouse lung from embryonic day (E) 16 to post-natal day (PND) 28 and discovered functionally distinct gene modules associated with each cell populations. These gene modules are analyzed further to identify their roles in lung development, specifically, how they contribute to cell-cell communication during lung development. In the second part of the thesis, I focus on a lethal rare lung disease, idiopathic pulmonary fibrosis (IPF), and identify molecular signatures that not only explain the heterogeneous nature of IPF but also the potential molecular basis of disease severity. I analyzed a large cohort of IPF data and identified clinically significant subgroups using only transcriptomic data. Lastly, I combined connectivity mapping and systems biology-based approaches to identify and prioritize candidate therapeutics for another rare lung disorder – cystic fibrosis (CF). We identified PP-2, a src-kinase inhibitor as a novel CFTR modulator that could potentially correct F508del CFTR in CF. We validated our finding in various in vitro cell-based assays. In summary, I developed a pipeline for single-cell RNA seq, a generalizable workflow for connectivity-based drug screening and a web database for single-cell data. With the developed tools and methods, I Identified potential therapeutic drugs for CF and repurposing drug candidates for IPF.
Bruce Aronow, Ph.D. (Committee Chair)
Anil Jegga (Committee Member)
Harinder Singh (Committee Member)
Jeffrey Whitsett, M.D. (Committee Member)
Kathryn WiWikenheiser-Brokamp, M.D., Ph.D. (Committee Member)
107 p.
Bioinformatics
Machine learning; Data mining; Lung development; Idiopathic pulmonary fibrosis; Drug discovery; Cystic fibrosis

Recommended Citations

Citations

  • Wang, Y. (2018). Data-driven Approaches to Understand Development, Diseases and Identify Therapeutics [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535704902199176

    APA Style (7th edition)

  • Wang, Yunguan. Data-driven Approaches to Understand Development, Diseases and Identify Therapeutics. 2018. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535704902199176.

    MLA Style (8th edition)

  • Wang, Yunguan. "Data-driven Approaches to Understand Development, Diseases and Identify Therapeutics." Doctoral dissertation, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535704902199176

    Chicago Manual of Style (17th edition)

ucin1535704902199176
442
© 2018, some rights reserved.Creative Commons License Data-driven Approaches to Understand Development, Diseases and Identify Therapeutics by Yunguan Wang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.