An empirical analysis of training protocols for probabilistic gene finders
BACKGROUND: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been...
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| Publicado no: | BMC Bioinformatics |
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| Principais autores: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BMC
2004
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.govhttps://pmc.ncbi.nlm.nih.gov/articles/PMC544851/ https://ncbi.nlm.nih.govhttps://pubmed.ncbi.nlm.nih.gov/15613242/ https://ncbi.nlm.nih.govhttps://doi.org/10.1186/1471-2105-5-206 |
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