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

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...

Full description

Bibliographic Details
Main Authors: Lee, J.-Y (Author), Li, J. (Author), Liao, L. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Active Learning
algorithm
Algorithms
article
Bioinformatics
Biological problems
Biological sequence data
Biological sequences
biology
Computational Biology
Constrained Baum-Welch algorithm
Expectation - maximizations
Functional domains
Functional proteins
genetics
hidden Markov model
Hidden Markov model
Hidden Markov models
learning
Learning systems
Markov chain
Markov Chains
Maximum likelihood
Maximum principle
mining
Partial label
plasmodesma
protein
Proteins
Signal detection
Text mining
Training methods
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items