Full metadata
Title
Fine Mapping Functional Noncoding Genetic Elements Via Machine Learning
Description
All biological processes like cell growth, cell differentiation, development, and aging requires a series of steps which are characterized by gene regulation. Studies have shown that gene regulation is the key to various traits and diseases. Various factors affect the gene regulation which includes genetic signals, epigenetic tracks, genetic variants, etc. Deciphering and cataloging these functional genetic elements in the non-coding regions of the genome is one of the biggest challenges in precision medicine and genetic research. This thesis presents two different approaches to identifying these elements: TreeMap and DeepCORE. The first approach involves identifying putative causal genetic variants in cis-eQTL accounting for multisite effects and genetic linkage at a locus. TreeMap performs an organized search for individual and multiple causal variants using a tree guided nested machine learning method. DeepCORE on the other hand explores novel deep learning techniques that models the relationship between genetic, epigenetic and transcriptional patterns across tissues and cell lines and identifies co-operative regulatory elements that affect gene regulation. These two methods are believed to be the link for genotype-phenotype association and a necessary step to explaining various complex diseases and missing heritability.
Date Created
2020
Contributors
- Chandrashekar, Pramod Bharadwaj (Author)
- Liu, Li (Thesis advisor)
- Runger, George C. (Committee member)
- Dinu, Valentin (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
107 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.62961
Level of coding
minimal
Note
Doctoral Dissertation Biomedical Informatics 2020
System Created
- 2021-01-14 09:16:07
System Modified
- 2021-08-26 09:47:01
- 3 years 3 months ago
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