A new algorithm to train hidden Markov models for biological sequences with partial labels

BACKGROUND: Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when...

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Detalles Bibliográficos
Autores principales: Li, Jiefu, Lee, Jung-Youn, Liao, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995745/
https://www.ncbi.nlm.nih.gov/pubmed/33771095
http://dx.doi.org/10.1186/s12859-021-04080-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995745/
https://www.ncbi.nlm.nih.gov/pubmed/33771095
http://dx.doi.org/10.1186/s12859-021-04080-0

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