Graduate Thesis Or Dissertation

 

Computational Approaches for RNA Structure Prediction with Dynamic Programming and Deep Neural Networks Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/8049gc157

Descriptions

Attribute NameValues
Creator
Abstract
  • Our goal is to build a system to model the RNA sequences that reveals their structural information by using efficient dynamic programming algorithms and deep learning approaches. We aim to 1) achieve linear-time for RNA secondary structure prediction based on existing minimum free energy models; 2) utilize deep neural networks to learn high-level features directly from RNA sequences without looking at any indirect information from MFE models, in order to predict RNA secondary structures directly; 3) we also investigate RNA structure visualization approaches. Here we organize our line of research all the way from a novel annotated dataset to systematic RNA secondary structure prediction using deep learning, including bpRNA (a RNA structure annotation tool with its generated, large-scale RNA meta-database bpRNA-1m), LinearFold (a linear-time dynamic programming algorithm for RNA secondary structure prediction), DeepSloop (a deep learning approach that learns complex rules to detect stem-loop-forming RNA sequences), and DeepStructure (an end-to-end RNA secondary structure prediction approach via deep neural networks). We also presented bpRNA-Visual for RNA structure visualization purposes.
License
Resource Type
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Rights Statement
Publisher
Peer Reviewed
Language
Embargo reason
  • Ongoing Research
Embargo date range
  • 2019-12-13 to 2020-07-13

Relationships

Parents:

This work has no parents.

In Collection:

Items