A Complementary Review of Data-based Clustering Model and Data Analysis for Gene Expressions

Authors(2) :-K L V G K Murthy, Dr. R. J. Rama Sree

Current microarray technology provides ways in which to get time-series expression knowledge for learning a large vary of biological systems. However, the expression knowledge tends to contain respectable noise that as a result might deteriorate the clustering quality. We tend to propose a knowledge-based clustering technique to include the information of gene-gene relations into the clustering procedure. Our technique first obtains the biological roles of every gene through a web mining process, next to teams genes supported their biological roles and also the gene ontology, and last applies a semi-supervised clustering model wherever the oversight is provided by the detected gene groups. Under the steerage of the information, the clustering procedure is able to address knowledge noise. We tend to evaluate our technique on an in public offered data set of human fibroblast response to serum. The experimental results demonstrate improved quality of clustering compared to the clustering strategies without any previous knowledge.

Authors and Affiliations

K L V G K Murthy
CSE Department, St. Marys Group of Institutions, Guntur, Research Scholar of Rayalaseema University, Kurnool, India
Dr. R. J. Rama Sree
Professor&Head, Department of Computer Science, Rashtriya Sanskrit Vidya Peeth, Tirupathi, Research supervisor for Rayalaseema University,Kurnool, India

Microarray Technology; Clustering; Data Analysis Gene Expression.

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2017
Date of Publication : 2017-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1126-1133
Manuscript Number : IJSRST1738181
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

K L V G K Murthy, Dr. R. J. Rama Sree, " A Complementary Review of Data-based Clustering Model and Data Analysis for Gene Expressions ", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 7, pp.1126-1133, September-October-2017.
Journal URL : https://ijsrst.com/IJSRST1738181
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