Construction Path analysis Model from DNA Microarray Data
Al Senousy, A., El-Deeb, B., Badran, C., Al-Khlil, D. (2012). Construction Path analysis Model from DNA Microarray Data. EKB Journal Management System, 8(8th International Conference on Electrical Engineering ICEENG 2012), 1-15. doi: 10.21608/iceeng.2012.30569
A. Mohmad Badr Al Senousy; B. Hesham Mohamed El-Deeb; C. Khaled Badran; D. Ibrahim Ali Al-Khlil. "Construction Path analysis Model from DNA Microarray Data". EKB Journal Management System, 8, 8th International Conference on Electrical Engineering ICEENG 2012, 2012, 1-15. doi: 10.21608/iceeng.2012.30569
Al Senousy, A., El-Deeb, B., Badran, C., Al-Khlil, D. (2012). 'Construction Path analysis Model from DNA Microarray Data', EKB Journal Management System, 8(8th International Conference on Electrical Engineering ICEENG 2012), pp. 1-15. doi: 10.21608/iceeng.2012.30569
Al Senousy, A., El-Deeb, B., Badran, C., Al-Khlil, D. Construction Path analysis Model from DNA Microarray Data. EKB Journal Management System, 2012; 8(8th International Conference on Electrical Engineering ICEENG 2012): 1-15. doi: 10.21608/iceeng.2012.30569

Construction Path analysis Model from DNA Microarray Data

The International Conference on Electrical Engineering
Article 3, Volume 8, 8th International Conference on Electrical Engineering ICEENG 2012, May 2012, Page 1-15  XML PDF (506.3 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2012.30569
View on SCiNiTO View on SCiNiTO
Authors
A. Mohmad Badr Al Senousy1; B. Hesham Mohamed El-Deeb2; C. Khaled Badran3; D. Ibrahim Ali Al-Khlil4
1Prof. Dr. in Sadat Academy for Management Science (SAMS), Department of Computer science.
2Dr. in Modern University for Technology and Information (M.T.I), Department of Computer science.
3Military Technical College (M.T.C), Department of Computer science.
4Military Technical Collage (M.T.C ), Department of Computer science.
Abstract
A fundamental problem in human health is to predict the effect of genes that cause disease; this is an important step to diagnosis and treatment. Also prediction gene functions are still a challenge for biologists in the post-genomic era. DNA microarray simultaneously monitors expression levels thousands of genes; the massive gene expression data provide us with unique opportunities to analyze the functional and regulatory relationships among genes. In this paper, new approach to estimate the relationship among genes and the effects of each gene on diseases is proposed. This approach consists of four main steps: First, extract the subset of high informative genes. Second, constructing genes network; thus, we propose to predict a gene’s functions according to its context graph, which is defined as the gene interaction network composed of the genes interacting directly and indirectly based on focal gene. Third, path analysis model is used to estimate the effect of genes on the disease and among each other. Fourth, obtains the relation among genes and the target (disease) by applying logistic regression analysis for predict new samples. This approach evaluated by lung cancer Microarray dataset. The proposed path diagram fit the subset of top ranked genes expression dataset, the Goodness of Fit Index (GFI) was > 0.832. The value of this approach is that it not only tackles the measurement problem by path
analysis but also provides a visualization of the relationship among genes. The proposed approach also is useful for feature reduction, due to evaluate all genes simultaneously vs. lung state.
Keywords
Microarray gene expression data; Genes selection; Genes ranking; Cancer; Genes network; path analysis; Pearson Correlation
Statistics
Article View: 122
PDF Download: 231