Evolving hidden Markov models for protein secondary structure prediction
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
New results are presented for the prediction of secondary structure information for protein sequences using Hidden Markov Models (HMMs) evolved using a Genetic Algorithm (GA). We achieved a Q 3 measure of 75% using one of the most stringent data set ever used for protein secondary structure prediction. Our results beat the best hand-designed HMM currently available and are comparable to the best known techniques for this problem. A hybrid GA incorporating the Baum-Welch algorithm was used. The topology of the HMM was restricted to biologically meaningful building blocks. Mutation and crossover operators were designed to explore this space of topologies.
Original language | English |
---|---|
Title of host publication | The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 : Proceedings |
Number of pages | 8 |
Volume | 3 |
Publisher | IEEE |
Publication date | 2005 |
Pages | 33-40 |
ISBN (Print) | 0-7803-9363-5 |
DOIs | |
Publication status | Published - 2005 |
Event | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom Duration: 2 Sep 2005 → 5 Sep 2005 |
Conference
Conference | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 |
---|---|
Land | United Kingdom |
By | Edinburgh, Scotland |
Periode | 02/09/2005 → 05/09/2005 |
ID: 199873169