Evolving hidden Markov models for protein secondary structure prediction
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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.
Originalsprog | Engelsk |
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Titel | The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 : Proceedings |
Antal sider | 8 |
Vol/bind | 3 |
Forlag | IEEE |
Publikationsdato | 2005 |
Sider | 33-40 |
ISBN (Trykt) | 0-7803-9363-5 |
DOI | |
Status | Udgivet - 2005 |
Begivenhed | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, Storbritannien Varighed: 2 sep. 2005 → 5 sep. 2005 |
Konference
Konference | 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 |
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Land | Storbritannien |
By | Edinburgh, Scotland |
Periode | 02/09/2005 → 05/09/2005 |
ID: 199873169