Predicting gene expression of yeast with deep learning-based sequence model vs linear regression model
Chang, Kehang
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/110276
Description
Title
Predicting gene expression of yeast with deep learning-based sequence model vs linear regression model
Author(s)
Chang, Kehang
Contributor(s)
Sinha, Saurabh,
Issue Date
2021-05
Keyword(s)
Sequence-to-expression
Convolutional neural network
Gene transcription modeling
Abstract
Predicting functional effects of enhancers in the gene transcription process has been an active
area of research to understand complex gene regulatory mechanisms and interpret non-coding
variants. Quantitative modeling of sequence-to-expression of organisms has wide applications
ranging from improvement in bioenergy products to better understanding of complex diseases
such as cancer. To demonstrate a sequence-to-expression modeling of a yeast called Issatchenkia
Orientalis, a data-driven convolutional neural network is trained, validated, and interpreted, and a
linear regression model is constructed to provide performance and result comparison. Both models
are being evaluated on real data of gene expressions of Issatchenkia Orientalis and provide unique
insights into underlying gene transcriptomic mechanisms in Issatchenkia Orientalis
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.