A Robust Method for Finding Somatic Mutations to form Clusters
T Bala Murali Krishna1, Anuradha Chokka2, S Phani Praveen3, K Venkatesh4
1T Bala Murali Krishna, Department of CSE, SSIET, Nuzvid (Andhra Pradesh), India.
2Anuradha Chokka, Department of IT, VITW, Vijayawada (Andhra Pradesh), India.
3S Phani Praveen, Department of CSE, PVPSIT, Vijayawada (Andhra Pradesh), India.
4K Venkatesh, Department of CSE, PVPSIT, Vijayawada (Andhra Pradesh), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1817-1823 | Volume-8 Issue-4, April 2019 | Retrieval Number: D7000048419/19©BEIESP
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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: Major goal of malignant (cancer) genomics is to pinpoint which physically changed qualities are engaged with tumor commencement and movement. The goal of this paper is to speak to all focuses in a high dimensional source space by focuses in a low dimensional target space by natural neural systems and to discover subspace clustering adequately and proficiently. Here a mechanism is applied to identify the somatic mutational genes in the form of mutational patterns to categorize clusters. To achieve this target a model based clustering method SOM2C is applied for effective clustering of high dimensional data. This proposed novel approach begins by taking 584 patients’ data from COSMIC, and processes the data and forms the somatic mutational genes in one cluster and non –cancerous cells in another cluster. The experimental results show breast cancerous related cancerous(somatic mutational) and non-cancerous clusters with Classification accuracy.
Keywords: Somatic Mutations, Breast Cancer, SOM2C, Mutational Patterns.
Scope of the Article: Classification