Detection of Motif in Protein Sequence Using K-Means and Fuzzy C-Means Algorithms

Authors

  • Geethamani R Department of Computer Science K.R College of Atrs And Science, Kovilpatti, Tamilnadu, India
  • Kalaivani B Department of Computer Science K.R College of Atrs And Science, Kovilpatti, Tamilnadu, India

Keywords:

clustering, k-means and fuzzy c-means, SMA

Abstract

Finding the Motif in biological sequences of protein synthesis is a basic problem in determining the protein structure. Detection of the Motif is used with many applications in gene regulation, protein family identification and determination of functionally and structurally important identities. Large amount of biological data is used to resolve the problem of discovering patterns in biological sequences computationally. In this research, we have designed an approach using a system of clustering in data mining to detect frequently occurring informative motifs that are high in information content. We have proposed a comparative approach for Skin Melanin associated problems(SMA) detection in preliminary stages using protein sequence. We have used the protein sequence with normal and abnormal data as the trained dataset. Test instances are classified into normal to abnormal by comparing it with the fundamental dataset. In this paper, We compare and evaluate the performance of two clustering algorithms namely K-means and fuzzy c-means clustering for protein sequences

References

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Published

2025-11-25

How to Cite

[1]
R. Geethamani and B. Kalaivani, “Detection of Motif in Protein Sequence Using K-Means and Fuzzy C-Means Algorithms”, Int. J. Comp. Sci. Eng., vol. 7, no. 8, pp. 87–90, Nov. 2025.

Issue

Vol. 7 No. 8 (2019): IJCSE Special Issue April Edition

Section

Survey Article

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