Discovery and identification of novel PD gene targets via an integrated data science and engineering approach.
Interdisciplinary Areas: | Data and Engineering Applications, Engineering-Medicine |
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Project Description
Neurodegenerative disorders, such as Parkinson’s disease (PD), are affecting a large percentage of population world-wide. No effective treatments are currently available thus presenting an urgent need for effective preventative and therapeutic strategy development. Extensive clinical and population studies have shown that PD has strong associations with both genetic and environmental factors. The genetic architecture underlying PD, and how it interacts with environmental factors, however, remain largely unknown. To address this knowledge gap, an enormous amount of data, such as genome-wide association study (GWAS) datasets on PD from NCBI the Database of Genotypes and Phenotypes (dbGaP), have been generated from both clinical studies and basic research. How to extract useful information from the large scale and heterogeneous data to identify novel genetic targets for PD treatment, however, remains a primary challenge. The goal of this project is to create a suite of data mining tools that can integrate multiple types of data in genome-wide association analysis; and validate part of the novel targets via state-of-the-art iPSCs and high-throughput imaging and electrophysiology assays to enhance our understanding of PD. The successful applicant is expected to work closely with data scientists and neurobiologists to pursue the goal of the project
Start Date
07/01/21
Postdoc Qualifications
Students with a PHD degree in biological engineering, biomedical engineering, chemical engineering or related disciplines are eligible to apply. Experience with data science, iPSCs or neuroscience is considered a plus.
Co-Advisors
Chongli Yuan, cyuan@purdue.edu, Chemical Engineering
Min Zhang, minzhang@purdue.edu, Statistics
References
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Horzmann KA, L.F. Lin, B. Taslakjian, C. Yuan, and J.L. Freeman. Embryonic atrazine exposure and later in life behavioral and brain transcriptomic, epigenetic, and pathological alterations in adult male zebrafish. . Cell Biology and Toxicology 10.1007/s10565-020-09548-y
Pungpapong V, Zhang M, Zhang D. (2019). Integrating biological knowledge into case-control analysis via iterated conditional modes/median algorithm. Journal of Computational Biology. PMID: 31692371. DOI: 10.1089/cmb.2019.0319. To appear.
Chen C, Zhang D*, Hazbun T*, Zhang M*. (*co-corresponding authors, 2019). Inferring gene regulatory networks from a population of yeast segregants. Scientific Reports. 9(1):1197.