About the Project
In this project, we propose to leverage big biological datasets from human samples and implement an integrative genomics approach based on deep neural networks and autoencoder models, to identify molecular mechanisms contributing to the cardiometabolic disorders Most of the previous genomic studies have focused on individual types of data (such as either genetic or transcriptomics), one at a time, and employed traditional statistical approaches to identify a few top significant GWAS signals which only provide fragmented and incomplete views of disease pathogenesis, whereas an integrative approach can utilize the power of collaborative use of various biological data types. In particular, this integrative approach will merge the summary statistics from the disease-associated GWASs and tissue-specific gene network models through the tissue-specific expression quantitative trait loci (eQTLs) signals, which correspond to another type of big-biological data resource, to prioritize the disease-associated subnetworks in a GRN and their hub genes that are potentially driving the disease progress. Hence, we purpose, not only elucidating the disease-mechanisms, but also predicting the hub (potential regulator) genes in these mechanisms. Therefore, our study may provide valuable guidance for testing new drug candidates, which target diseases-associated genes found from our study, on experimental animal models such as mouse or rats in pharmacological studies for cardiometabolic research. This interdisciplinary design incorporating computer science with biology, genetics, and medicine can significantly raise the quality of human lives.
The principal supervisor for this project is Dr. Zeyneb Kurt.
Eligibility and How to Apply:Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
For further details of how to apply, entry requirements and the application form, see
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. RDF21/EE/CIS/KURTZeyneb) will not be considered.
Deadline for applications: 29 January 2021
Start Date: 1 October 2021
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community.
I earned two post-doctoral fellowships in computational biology and bioinformatics research fields. These projects have been relevant to the proposed project:
1. 2017, American Heart Association (AHA) Postdoctoral Fellowship for 2 years.
2. 2017, University of California, Los Angeles (UCLA) Women’s Health Center Iris-Cantor/CTSI Young Investigator Award, Postdoctoral Fellowship for 1 year.
My top five journal articles relevant to the proposed project:
1. Kurt Z, Barrere-Cain R, Laguardia J, Mehrabian M, Pan C, et al. "Tissue-specific pathways and networks underlying sexual dimorphism in non-alcoholic fatty liver disease", Biology of Sex Differences 2018, vol. 9:46. doi: 10.1186/s13293-018-0205-7.
2. Krishnan KC*, Kurt Z*, Barrere-Cain R, Sabir S, Das A, et al. “Integration of Multi-omics Data from Mouse Diversity Panel Highlights Mitochondrial Dysfunction in Non-Alcoholic Fatty Liver Disease”, Cell Systems 2017. doi: 10.1016/j.cels.2017.12.006. *Shared (co-first) authors.
3. Hui S, Kurt Z, Tuominen L, Norheim F, Davis RC, et al. “The Genetic Architecture of Diet-induced Hepatic Fibrosis in Mice”, Hepatology 2018, vol.1, pp.1-20. doi: 10.1002/hep.30113.
4. Erdogan C, Kurt Z, Diri B, "Estimation of the proteomic cancer co-expression subnetworks by using association estimators", PLOS ONE 2017, doi:10.1371/journal.pone.0188016.
5. Kurt Z, Aydın N, and Altay G, “A Comprehensive Comparison of Association Estimators for Gene Network Inference Algorithms”, Bioinformatics: Oxford Journals 2014, vol. 30 (15), pp. 2142-2149. doi: 10.1093/bioinformatics/btu182
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