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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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GENE DATA CLUSTERING USING K-MEANS ALGORITHM
Prateek A. Meshram, Dr.P.P Halkarnikar, Dr. Amol Dhakne, Mrs. Pratiksha Shevatekar, Mr. Shivaji R. Vasekar, Mr. Jitendra Garud
Journal of Data Acquisition and Processing, 2023, 38 (2): 119-126 .
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Abstract
Gene expression data contents vital information about the biological process that takes place in a particular organism under specific environment. Gene expression data is vague, imprecise, and noisy. Therefor to get the information of gene states clustering is vital step. Gene expression clustering is used to find co-systematizes gene groups from large collection of gene, whose collective samples are equal to the expressions. Clustering gene expression data benefits in the identification of homology, this helps in vaccine design. There are many unsupervised clustering algorithms used for this purpose. In this paper we have selected yeast sporulation dataset for clustering. The cluster based on prominent features values convey maximum information of bioprocess developed. In order to cluster such a dataset with many features and large unknown patterns k-means algorithm is effective. Such fast pattern finding method is useful for detecting new viruses and drug simulation. The quality of cluster is important while analysis of gene expression. The effectiveness of our proposed algorithm we compared using adjusted rand index (ARI) with the previously reported algorithms and found satisfactory result on yeast sporulation dataset.
Keyword
gene expression, k-means clustering, bioinformatics, elbow method
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