Chao-Hsuan Ke

Chao-Hsuan Ke, Tsung-Lu Michael Lee, Jung-Hsien Chiang

Summary: Abstracts in biomedical articles can provide a quick overview of the articles but detailed information cannot be obtained without reading full-text contents. Full-text articles certainly generate more information and contents; however, accessing full-text documents is usually time consuming. Condensedly is a web-based application, which provides readers an easy and efficient way to access full-text paragraphs using sentences in abstracts as fishing bait to retrieve the big fish reside in full-text. Condensedly is based on the paragraph ranking algorithm, which evaluates and ranks full-text paragraphs based on their association scores with sentences in abstracts. Availability: http://140.116.247.185/~research/Condensedly

Li-Yeh Chuang, Chao-Hsuan Ke, Cheng-Hong Yang

Gene expression data is widely used in disease analysis and cancer diagnosis. However, since gene expression data could contain thousands of genes simultaneously, successful microarray classification is rather difficult. Feature selection is an important pre-treatment for any classification process. Selecting a useful gene subset as a classifier not only decreases the computational time and cost, but also increases classification accuracy. In this study, we applied the information gain method as a filter approach, and an improved binary particle swarm optimization as a wrapper approach to implement feature selection; selected gene subsets were used to evaluate the performance of classification. Experimental results show that by employing the proposed method fewer gene subsets needed to be selected and better classification accuracy could be obtained.