Predicting RNA secondary structure using a stochastic conjunctive grammar
In this thesis I extend a class of grammars called conjunctive grammars to a stochastic form called stochastic conjunctive grammars. This extension allows the grammars to predict pseudoknotted RNA secondary structure. Since observing sec- ondary structure is hard and expensive to do with today�...
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
2012
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1993/8453 |
| Summary: | In this thesis I extend a class of grammars called conjunctive grammars to a
stochastic form called stochastic conjunctive grammars. This extension allows the
grammars to predict pseudoknotted RNA secondary structure. Since observing sec-
ondary structure is hard and expensive to do with today's technology, there is a need for computational solutions to this problem. A conjunctive grammar can handle
pseudoknotted structure because of the way one sequence is generated by combining
multiple parse trees.
I create several grammars that are designed to predict pseudoknotted RNA sec-
ondary structure. One grammar is designed to predict all types of pseudoknots and
the others are made to only predict a pseudoknot called H-type. These grammars are
trained and tested and the results are collected. I am able to obtain a sensitivity of over 75% and a speci city of over 89% on H-type pseudoknots |
|---|