- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Representation learning strategies for the epigenome...
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Representation learning strategies for the epigenome and chromatin structure using recurrent neural models Dsouza, Kevin Bradley
Abstract
In this Ph.D. thesis, we propose frameworks for designing informative position-specific representations from epigenomic and structural genomic signals. We use recurrent priors in our analysis owing to the fact that the genome is heavily correlated with nearby positions, and implement them using recurrent neural models. We demonstrate that the representations we learn are helpful for various tasks, including, locating known genomic elements, identifying conserved sites, correlating with established genomic measures, enabling accurate decoding, finding elements that drive 3D conformation, attributing relative positional importance, and performing in-silico modifications. In the process of designing these representations, we study two classes of representation learning strategies that differ in their underlying philosophy, namely, autoencoding and categorical encoding. We show that the usefulness of these representations depends on the underlying strategies used while designing them.
Item Metadata
Title |
Representation learning strategies for the epigenome and chromatin structure using recurrent neural models
|
Creator | |
Supervisor | |
Publisher |
University of British Columbia
|
Date Issued |
2023
|
Description |
In this Ph.D. thesis, we propose frameworks for designing informative position-specific representations from epigenomic and structural genomic signals. We use recurrent priors in our analysis owing to the fact that the genome is heavily correlated with nearby positions, and implement them using recurrent neural models. We demonstrate that the representations we learn are helpful for various tasks, including, locating known genomic elements, identifying conserved sites, correlating with established genomic measures, enabling accurate decoding, finding elements
that drive 3D conformation, attributing relative positional importance, and performing in-silico
modifications. In the process of designing these representations, we study two classes of representation learning strategies that differ in their underlying philosophy, namely, autoencoding
and categorical encoding. We show that the usefulness of these representations depends on the
underlying strategies used while designing them.
|
Genre | |
Type | |
Language |
eng
|
Date Available |
2023-05-16
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
Attribution-ShareAlike 4.0 International
|
DOI |
10.14288/1.0432347
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Graduation Date |
2023-11
|
Campus | |
Scholarly Level |
Graduate
|
Rights URI | |
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-ShareAlike 4.0 International