Expression Profiling & Classification using Convolutional Neural Networks of Tumor Suppressor Genes Linked with Stress
Kaajal Nishandh1, Sanjay Kumar P2, P.K Krishnan Namboori3
1Kaajal Nishandh, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vidhya Peetham, Coimbatore (Tamil Nadu), India.
2Sanjay Kumar P, Amrita Molecular Modeling and Synthesis AMMAS Research Lab, Computational Engineering, and Networking, Amrita School of Engineering, Amrita Vidhya Peetham, Coimbatore (Tamil Nadu), India.
3P.K Krishnan Namboori, Molecular Modeling and Synthesis AMMAS Research Lab, Computational Engineering, and Networking, Amrita School of Engineering, Amrita Vidhya Peetham, Coimbatore (Tamil Nadu), India.
Manuscript received on 06 February 2019 | Revised Manuscript received on 19 February 2019 | Manuscript Published on 04 March 2019 | PP: 302-305 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES2053017519/19©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: Tumor suppressor genes are always linked with stress, directly or indirectly which results in mutation. Therefore the probability of turning these mutations into cancer increases. Identification of major tumor suppressor genes and its presence among Indian population is analyzed. Due to great advancement in the field of deep learning, and wide variety of scopes in future, deep learning is incorporated in this project to perform the classification task .The requirement of large amount of data to perform classification task is one of the major drawback of deep learning. In order to solve this problem, one-shot learning algorithm is introduced which gave the accuracy of 70.2%. A secure data sharing platform has been developed using blockchain technique.
Keywords: Block Chain Technique, Deep Learning, Tumor Suppressor Genes.
Scope of the Article: Neural Information Processing