Hidden Markov Models and their Applications in Biological Sequence Analysis

By Byung-Jun Yoon. Current Genomics, 2009

387 citations recorded by the source corpus.

Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f7181dd96101b60fd397ef5d53c152b56fbff056

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In Molecular Biology

1. Bin Liu
2. Junjie Chen
3. Xiaolong Wang
4. Mingyue Guo

Citations in the corpus

• A comprehensive review and comparison of different computational methods for protein remote homology detection (2018, methodology): “HMM transforms a multiple sequence alignment into a position-specific scoring system [47], which cannot only”