| 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. |
