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Augmented training of hidden Markov models to recognize remote homologs via simulated evolution
Motivation: While profile hidden Markov models (HMMs) are successful and powerful methods to recognize homologous proteins, they can break down when homology becomes too distant due to lack of sufficient training data. We show that we can improve the performance of HMMs in this domain by using a sim...
| Autores principales: | , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2009
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732314/ https://www.ncbi.nlm.nih.gov/pubmed/19389731 http://dx.doi.org/10.1093/bioinformatics/btp265 |