Frequent Sequential Patterns (FSP) Algorithm for Finding Mutations in BRCA2 Gene
Jawahar. S1, Reshmi. S2, Ahamed Johnsha Ali. S3
1Jawahar. S, Assistant Professor, Department of Computer Science and Applications, Sri Krishna Arts and Science College, Coimbatore- 641008.
2Reshmi. S, Assistant Professor, Department of Computer Science and Applications, Sri Krishna Arts and Science College, Coimbatore- 641008.
3Ahamed Johnsha Ali. S, Assistant Professor, Department of IT and CA Sri Krishna Adithya College of Arts and Science Coimbatore- 641008.
Manuscript received on 10 August 2019. | Revised Manuscript received on 17 August 2019. | Manuscript published on 30 September 2019. | PP: 8585-8586 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6507098319/19©BEIESP | DOI: 10.35940/ijrte.C6507.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Sequential Pattern Mining (SPM) is a fundamental task in data mining. The SPM mines subsequences from given sequence which can be used for various analyses. This paper aims to propose an efficient method for mining frequent sequential patterns in biological data. It also includes the k-mer for decomposing the sequence according to the user defined threshold value. The input data used is breast cancer gene BRCA2 normal and mutated BRCA2 gene. The parameters used for analyses are suffix, candidate pattern and frequent pattern. The suffix value is increased for mono-,di and tri-nucleotide in mutated gene and in frequent pattern tri-nucleotide has increased nucleotide in mutated gene. So this abnormal increase in pattern may leads to cancer in the human
Keywords- Sequential Pattern Mining (SPM), k-mer, BRCA2, Suffix
Scope of the Article: Sequential, Parallel and Distributed Algorithms and Data Structures