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Characterization of Parathyroid Hormone using HMM Framework

by Raninder Kaur, Shavinder Kaur, Reet Kamal Kaur, Amandeep Kaur Sohal
journal cover thumbnail
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 16
Year of Publication: 2010
Authors: Raninder Kaur, Shavinder Kaur, Reet Kamal Kaur, Amandeep Kaur Sohal
10.5120/340-518

Raninder Kaur, Shavinder Kaur, Reet Kamal Kaur, Amandeep Kaur Sohal . Characterization of Parathyroid Hormone using HMM Framework. International Journal of Computer Applications. 1, 16 ( February 2010), 65-68. DOI=10.5120/340-518

@article{ 10.5120/340-518,
author = { Raninder Kaur, Shavinder Kaur, Reet Kamal Kaur, Amandeep Kaur Sohal },
title = { Characterization of Parathyroid Hormone using HMM Framework },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 16 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 65-68 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number16/340-518/ },
doi = { 10.5120/340-518 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:44.404757+05:30
%A Raninder Kaur
%A Shavinder Kaur
%A Reet Kamal Kaur
%A Amandeep Kaur Sohal
%T Characterization of Parathyroid Hormone using HMM Framework
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 16
%P 65-68
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sequence comparison is a key tool in bioinformatics. The objective of a HMM profile is to statistically model patterns in biological sequences by identifying combinations of matches, in-dels, and gaps in the alignment of a query sequence to a profile model. HMM profile analysis can be used for multiple sequence alignment, for a database search, to analyze sequence composition and pattern segmentation, and to predict protein structure and locate genes by predicting open reading frames. Profile analysis has long been a useful tool in finding and aligning distantly related sequences and in identifying known sequence domains in new sequences. Hidden Markov modeling, a technique that has been used for years in speech recognition, is now being applied to many types of problems in molecular sequence analysis. In particular, this technique can produce profiles that are an improvement over traditionally constructed profiles.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Bioinformatics HMM biological

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