A Complete Summary of Non-Parametric Statistical Methods Used For Biological Microarray Data
Meenu Sharma1, Rafat Parveen2
1Meenu Sharma*, Department of Computer Science, Jamia Millia Islamia, Jamia Nagar New Delhi, India.
2Dr. Rafat Parveen, Department of Computer Science, Jamia Millia Islamia, Jamia Nagar New Delhi, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4995-5002 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8127118419/2019©BEIESP | DOI: 10.35940/ijrte.D8127.118419
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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: Microarray technology is developed as a new powerful biotechnology tool, to analyze the expression profile of more than thousands of genes simultaneously. In recent times, Microarray is the most popular research topic. For extracting the differentially expressed genes from microarray data, numerous types of statistical tests are developed. The focus of microarray analysis is to predict genes that show different expression patterns under two different experimental conditions. The aim of this research paper is to explore various types of non-parametric methods proposed to analyze microarray expression data for predicting those genes which are differentially expressed, and a comparative analysis of various methods has been done. Besides, we also predicted the best condition for each method where they perform better and to investigate the disease development mechanism. Many types of statistical tests have been studied for identifying the differentially expressed genes, only very few studies have compared the performance of these methods. In our study, we extensively study and compare the different types of non-parametric methods.
Keywords: About Four Key Words r Phrases In Alphabetical Order, Separated By Commas.
Scope of the Article: Bio-Science and Bio-Technology.