Applying the Semantic Analysis in Cancer-Related Genes Mining and Prediction Integrated System

ASIAIR > College of Computer Science > Department of Biomedical informatics > Theses & dissertations > Item 310904400/11498

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/11498

Title:	Applying the Semantic Analysis in Cancer-Related Genes Mining and Prediction Integrated System
Authors:	CHIEN-LI CHEN
Contributors:	Department of Biomedical informatics?
Keywords:	cancer-related gene;loss of heterozygosity (LOH);comparative genomic hybridization (CGH);biomedical literature
Date:	2011
Issue Date:	2011-09-26 02:39:47 (UTC+0)
Publisher:	Asia University
Abstract:	Abstract 　　With the upgrading of the national standard of living and eating habits change, in recent years, the incidence of cancer increased year-by-year. According to the statistics of ten major causes of death published by the Bureau of Health Promotion, Department of Health, R.O.C. (Taiwan), cancer is a leading cause of death for twenty-nine consecutive years. The face of the growing threat of cancer, it is important to study the cause of cancer. With advances in the Human Genome Project, researchers are increasingly becoming engaged in bioinformatics-related research, including genome sequence analysis, drug design and discovery, and curative methods. The published literature contains a wealth of information, such as gene and gene expression, gene and function, biopathway, gene and disease relationship. However, while biomedical researchers how to search and retrieve worthy of study information in biomedical literature, there is a problem of information overloading. 　　The purpose of this study is to develop a biomedical literature mining platform to predict cancer-related genes. The platform applied semantic analysis technology to increase the prediction accuracy of cancers, genes, and chromosome regions. Several value-added databases are constructed to achieve this purpose. They contain information of genes in the instable regions of cancer cells basing on the data accumulated from LOH and CGH experiments. This proposed platform can extract important information to accelerate the study and save plenty of time for biomedical researchers. Besides, this system can also be used on other diseases.
Appears in Collections:	[Department of Biomedical informatics ] Theses & dissertations

Files in This Item:

File	Size	Format
	0Kb	Unknown	504	View/Open

Loading...