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PROMINENT: AN INTERPRETABLE DEEP LEARNING METHOD TO PREDICT PHENOTYPES USING DNA METHYLATION

Zhang, Laizhi (2024) PROMINENT: AN INTERPRETABLE DEEP LEARNING METHOD TO PREDICT PHENOTYPES USING DNA METHYLATION. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

DNA methylation (DNAm) is an epigenetic mark that regulates genome function and is important for normal development in mammals. To understand the complex interplay between genetics, DNA methylation, and phenotype, computational approaches have emerged to uncover DNAm marker-phenotype associations, often employing regression techniques, such as Methylation Risk Scores (MRS) or methylation profile scores (MPS). Recently, a deep neural network (DNN) method, MethylNet was proposed to predict traits based on DNAm array data based on variational auto encoder. However, understanding the association between DNAm data and phenotypes in the DNN architecture remains challenging since MethylNet does not directly incorporate the relationship between methylation and genes. Also, its high demand for computation time necessitates meticulous tuning of important model parameters to achieve optimal performance, making it difficult to optimize data training. To overcome these challenges, we introduce Pathway Information-based Methylation Neural Network (PROMINENT). PROMINENT incorporates gene-level DNA marker information and gene pathway priors to enhance prediction accuracy and interpretability. In a comprehensive comparison using asthma, idiopathic pulmonary fibrosis (IPF), and first-episode psychosis (FEP) data, PROMINENT shows promise in predicting phenotypes using DNA methylation data with reasonable computational time.

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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhang, LaizhiLAZ64@pitt.edulaz640000-0002-4489-7038
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKim, Soyeonsok54@pitt.edusok54
Committee MemberPark, Hyun Junghyp15@pitt.eduhyp15
Committee MemberMinster, Ryanrminster@pitt.edurminster
Date: 16 May 2024
Date Type: Publication
Defense Date: 18 April 2024
Approval Date: 16 May 2024
Submission Date: 25 April 2024
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 41
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Human Genetics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: DNA Methylation, Deep Learning
Date Deposited: 16 May 2024 19:20
Last Modified: 16 May 2024 19:20
URI: http://d-scholarship.pitt.edu/id/eprint/46319

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