英文摘要 |
Understanding the relationships between drug activities and genes are important in the treatment of diseases. Using the right medicines to cure the diseases at their early stage will be more effective. With the development of new medicines, the amount of medicines used to cure the same diseases is increasing. Some medicines are also capable of curing different diseases. In clinical practices, doctors need to use the effective medicines for a specific disease. Two datasets are especially useful in the study of the relationships between drug activities and gene expression of cancers. One is the relationships between drug activities and cancers, and the other is gene expression verse cancers. In this paper, we integrated both datasets into a drug activities and gene expression system using four analysis techniques. The goal of this study is to design a system platform to display various relationships among drug activities, gene expression, and cancers. These graphical interfaces are developed by Matlab. The relationships between medicines and gene expression profiles were constructed based on four analysis techniques, including the K2 algorithm, the K-means algorithm, the Pearson correlation analysis technique, and the Hierarchical clustering technique. The system can not only generate the most effective group of medicines for any selected cancers but also can output drugs to genes Microsoft EXCEL (XLS) files to medical professionals. |