Date of Award

Summer 8-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

Program/Concentration

Computer Science

Committee Director

Jiangwen Sun

Committee Member

Desh Ranjan

Committee Member

Jing He

Committee Member

Yet Nguyen

Abstract

Epigenetic events, such as DNA methylation and histone modifications, arise from a complex interplay among genomic sequence, chromatin-remodeling factors, and environmental cues. These regulatory mechanisms can induce changes in gene expression without altering the underlying DNA sequence, playing critical roles in development, disease, and cellular differentiation. Among these events, DNA methylation is frequently profiled using bisulfite sequencing (e.g., whole-genome bisulfite sequencing [WGBS], reduced representation bisulfite sequencing [RRBS]). However, predictive modeling of epigenetic states—including methylation patterns and regulatory variant effects—remains challenging due to data sparsity, label noise, and limited uncertainty estimation in current deep learning approaches. This dissertation addresses these issues by introducing a suite of data-centric and uncertainty-aware deep learning frameworks. First, I conduct a systematic evaluation to quantify how data quality, sample size, and label noise affect model performance. Second, I propose a transfer learning method to impute sparse methylation profiles to improve data quality. Third, I develop a Monte Carlo dropout–based pipeline that quantifies uncertainty for non-coding genetic variant effect predictions, aiding in the prioritization of potentially regulatory variants in tissue-specific contexts. Collectively, these contributions advance scalable, interpretable, and reliable computational approaches for epigenomic data analysis, paving the way for improved understanding and practical utilization of epigenetic events in developmental and disease settings.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/zsma-bk24

ISBN

9798293842377

ORCID

0000-0002-0198-1264

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