International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7 Issue-5S2, January 2019 302 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: ES2053017519/19©BEIESP 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. Index Terms: Block Chain Technique, Deep Learning, Tumor suppressor genes. I. INTRODUCTION Stress is a typical natural response to dangerous situations of any kind of demand or threat, which is accompanied with a number of chemical and biological aberrations and resulted by the release of chemicals and hormones such as adrenaline and cortisol. In a biomolecular perspective, it is found to be extended even up to the cellular level. Cellular stresses is generally resulted out of the cells being exposed to external and internal stressors which damages the integrity of the cell and its genome such as cigarette smoke, hypoxia, ionizing radiations, oxidative stress, carcinogens, oncogene activation etc. [1]. Apart from mental and physical stresses, another main stress is the cellular stress. Cellular stresses can happen when human cell is exposed to external and internal stressors which damages the integrity of the cell and its genome such as cigarette smoke, hypoxia, ionizing radiations, oxidative stress, carcinogens, oncogene activation etc. [1]. This can lead to damage of DNA and also malignant transformation of cells. To ensure the survival of the organisms, cells have built up various methodologies to adjust to stressors i.e. the “Tumor Suppressor Genes” [2]. Due to this evolving nature of biological systems, it becomes difficult to carry out the big data analysis using the basic techniques. Revised Manuscript Received on January 25, 2019 Kaajal Nishandh, Department of Electronics and Communication Engineerining, Amrita School Of Engineerining, Amrita Vidhyapeetham, Coimbatore, India. Sanjay Kumar P, Amrita Molecular modeling and synthesis (AMMAS) Research Lab, Computational Engineerining and Networking, Amrita School Of Engineerining, Amrita Vidhyapeetham, Coimbatore, India. P.K Krishnan Namboori, Molecular modeling and synthesis (AMMAS) Research Lab, Computational Engineerining and Networking, Amrita School Of Engineerining, Amrita Vidhyapeetham, Coimbatore, India. This gap could be bridged using emerging machine learning techniques such as deep learning which works with huge data sets. With the implementation of Siamese net or One- shot learning, one could carry out the classification problem with minimal data with better prediction accuracy [3]. Gene expression analysis is important to understand the association of tumor suppressor genes in the regulation of the disease. The ‘gene expression profiling’ can be considered as the ‘mutation signature’ of the disease. Although gene expression profiling is presently used as a primary research tool, many other potential clinical applications of this method are being instigated. The expression profile renders prognostic data about the aggressiveness nature of tumor, response to therapies, sensitivity and resistance to different chemotherapies [4]. The expression level varies from person to person, demanding a pharmacogenomic investigation in the subject [4] [5]. The knowledge of pharmacogenomic with genomic, epigenomic, metagenomic and environmental genomic components, helps in developing safe and effective medications according to the person’s genetic composition. The proneness of the disease as well as the susceptibility of drugs depends up on the individual variations. Epigenomics deals with the study of the entire set of epigenetic alterations on the genetic material of a cell (epigenome), Metagenomics is the study of genetic material taken from micro-organisms such as bacteria, yeast etc and environmental genomics deals with the prediction of how the organisms respond to external environmental factors [6]. Pharmacogenomics promises personalized treatment for patients suffering from common diseases, especially with multiple treatment modalities [7]. Pharmacogenomics provides the population specific marker frequency profiles and also drug efficiency. Various machine learning applications in healthcare helps in early diagnosis and prediction of suitable treatment strategy to the patient. Recently, deep learning algorithm has been identified as most suitable for identifying cancerous tumors from mammogram. At present, training the machine with minimum number of samples could be possible with modern learning experiences such as ‘one shot learning or Siamese net’. One-shot learning ranks the similarity of inputs in which the classification is done using K-nearest neighbor calculating the Euclidean distance between the test and train data, classifying the nearest ones as in the same class. Since health care deals with confidential patient Expression Profiling & Classification using Convolutional Neural Networks of Tumor Suppressor Genes Linked with Stress Kaajal Nishandh, Sanjay Kumar P, P.K Krishnan Namboori