Subject: |
RNA Secondary Structure |

Presenter: |
Mirela Andronescu |

Abstract |
Towards RNA Thermodynamic Parameter Estimation by Iterative Scaling
Algorithms
The current best algorithm for RNA secondary structure prediction
achieves an accuracy of about 70% for pseudoknot-free secondary
structures. Part of this weak performance is due to the underlying
thermodynamic model, which is incomplete and/or inaccurate. The parameters
used in this model have been determined by enormous work of some
biochemistry labs over several years. In this talk I am going to propose a
computational model to predict the RNA thermodynamic parameters. The
problem is defined as follows: given a large set of RNA sequences with
known MFE secondary structures, determine the best underlying
thermodynamic parameters for RNA secondary structure prediction. I propose
to use "Iterative scaling algorithm", a supervised learning algorithm
that has been used successfully for parameter estimation for other
exponential models. |