Graduate Project
 

RNA Secondary Structure Prediction Using Neural Machine Translation

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_projects/pv63g4248

Descriptions

Attribute NameValues
Creator
Abstract
  • RNA secondary structure prediction maps a RNA sequence to its secondary structure (set of AU, CG, and GU pairs). It is an important problem in computational biology be-cause such structures reveals crucial information about the RNAs function, which is useful in many applications ranging from noncoding RNA detection to folding dynamics simulation. Traditionally, RNA structure prediction is often accomplished computationally by the cubic-time CKY parsing algorithm borrowed from computational linguistics, with the energy parameters either estimated physically or learned from data. With the advent of deep learning, we propose a brand-new way of looking at this problem, and cast it as a machine translation problem where the RNA sequence is the source language and the dot-parenthesis structure is the target language. Using a state-of-the-art open source neural machine translation package, we are able to build an RNA structure predictor without any hand-designed features.
License
Resource Type
Date Available
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Rights Statement
Publisher
Peer Reviewed
Language
Replaces

Relationships

Parents:

This work has no parents.

Items

Thumbnail Title Date Uploaded Visibility Actions
file details ZhangLuyao2016.pdf 2017-10-05 Public Download