Biological Analysis and Linear Block Hidden Markov Model for Gene and Labelled

Authors

  • Dr. Suneel Pappala

Keywords:

hidden markov model (HMM), pair-hmm, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analys

Abstract

Hidden Markov models (HMMs) have been extensively used in biological sequence analysis . HMMs and their applications in a variety of problems in molecular biology .The difficulty of using computational approaches to discover genes in DNA sequences is yet unsolved. gene prediction from within genomic DNA are far from being powerful enough to elucidate the gene structure completely. We develop a hidden Markov model (HMM) to represent the degeneracy features of splicing junction donor sites in eucaryotic genes. he HMM system is fully rained using an expectation maximization algorithm and the system performance is evaluated using the 10-way cross-validation method. he HMM system is fully trained using an expectation maximization algorithm and the system performance is evaluated using the 10-way cross-validation method.

Downloads

How to Cite

Dr. Suneel Pappala. (2022). Biological Analysis and Linear Block Hidden Markov Model for Gene and Labelled. Global Journal of Computer Science and Technology, 22(H1), 13–17. Retrieved from https://computerresearch.org/index.php/computer/article/view/101451

Biological Analysis and Linear Block Hidden Markov Model for Gene and Labelled

Published

2022-07-19