An Approach in Big Data Analytics Framework for Analysing Huge Gene Transcription Data
Rohit Roy1, S. P. Syed Ibrahim2
1Rohit Roy, SCSE, VIT University, Chennai, India.
2Dr. S P Syed Ibrahim, SCSE, VIT University, Chennai, India.
Manuscript received on April 30, 2019. | Revised Manuscript Received on May 22, 2019. | Manuscript published on June 20, 2019. | PP: 1-4 | Volume-2 Issue-7, June 2019 | Retrieval Number: G0089052719
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Gene Co-expression network analysis is increasingly used to explore system level functionality. In order to study the complexity within gene interactions and identify a target gene for clinical application the researchers apply co expression network based on correlation coefficient. Significance of weighted co expression network analysis is that it reduces the high dimension of the data and integrity of multi scale dataset and also identifies the hidden interactions among the genes. Construction of co expression network on a large sample size would improve the accuracy and robustness but statistical and computational methods applied for screening of multidimensional data are both space and time consuming. In present, the researchers are at the verge of acquiring a new methodology that analyzes huge data in short time period. However big data analytics method for analyzing gene co expression network is in infant stage. Our objective is to identify an approach using big data analytics framework that can enable scientific research community to process large scale data and support them in identifying clinically significant targets.
Keywords: Co-Expression, Correlation, Weighted Network, Network Analysis.
