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JMBS 2018, 9(2): 259-265 |
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Paylakhi S, Ozgoli S, Paylakhi S. Introduction of GRAP Gene as Alzheimer's Disease Candidate Gene Using Microarray Data Analysis. JMBS 2018; 9 (2) :259-265
URL: http://biot.modares.ac.ir/article-22-14248-en.html
URL: http://biot.modares.ac.ir/article-22-14248-en.html
1- Control Department, Electrical & Computer Engineering Faculty, Tarbiat Modares University, Tehran, Iran
2- Control Department, Electrical & Computer Engineering Faculty, Tarbiat Modares University, Tehran, Iran, Tarbiat Modares University, Nasr Bridge, Jalal-Al-Ahmad Highway, Tehran, Iran , ozgoli@modares.ac.ir
3- Ophthalmology Department, Ophthalmology Faculty, University of California, San Francisco, United States
2- Control Department, Electrical & Computer Engineering Faculty, Tarbiat Modares University, Tehran, Iran, Tarbiat Modares University, Nasr Bridge, Jalal-Al-Ahmad Highway, Tehran, Iran , ozgoli@modares.ac.ir
3- Ophthalmology Department, Ophthalmology Faculty, University of California, San Francisco, United States
Abstract: (4876 Views)
Aims: One of the most important areas in medical research is the identification of disease-causing genes, which helps the identification of mechanisms underlying disease and as a result helps the early diagnosis of disease and the better treatment. In recent years, microarray technology has assisted biologists to gain a better understanding of cellular processes. To this end, the application of efficient methods in microarray data analysis is very important. The aim of this study was the introduction of GRAP Gene as Alzheimer’s disease candidate gene using microarray data analysis.
Materials and Methods: In the present bioinformatic study, which was conducted on an Alzheimer's microarray data set containing 12990 genes, 15 patients, and 16 healthy subjects, by combining Fisher, Significance Analysis of Microarray (SAM), and Particle Swarm Optimization (PSO) methods as well as Classification and Regression Tree (CART), a new method was presented for analyzing microarray gene expression data to identify genes involved in Alzheimer's incidence.
Findings: The accuracy level of the proposed method was 90.32% and the interpretation of the results from a biological point of view indicated that the proposed method has worked well; finally, the proposed method introduced 4 genes, of which, until now, 3 genes (75%) have been reported in biological studies as genes that cause Alzheimer’s disease.
Conclusion: In addition to proposing a new feature selection method for the analysis of microarray data, this study has introduced a new gene (GRAP) as a candidate gene related to Alzheimer’s disease.
Materials and Methods: In the present bioinformatic study, which was conducted on an Alzheimer's microarray data set containing 12990 genes, 15 patients, and 16 healthy subjects, by combining Fisher, Significance Analysis of Microarray (SAM), and Particle Swarm Optimization (PSO) methods as well as Classification and Regression Tree (CART), a new method was presented for analyzing microarray gene expression data to identify genes involved in Alzheimer's incidence.
Findings: The accuracy level of the proposed method was 90.32% and the interpretation of the results from a biological point of view indicated that the proposed method has worked well; finally, the proposed method introduced 4 genes, of which, until now, 3 genes (75%) have been reported in biological studies as genes that cause Alzheimer’s disease.
Conclusion: In addition to proposing a new feature selection method for the analysis of microarray data, this study has introduced a new gene (GRAP) as a candidate gene related to Alzheimer’s disease.
Article Type: Research Paper |
Subject:
Agricultural Biotechnology
Received: 2016/10/25 | Accepted: 2017/12/11 | Published: 2018/06/21
Received: 2016/10/25 | Accepted: 2017/12/11 | Published: 2018/06/21
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