Visualization of transitions of developing of hepatitis C virus-associated hepatocellular carcinoma

In our previous study, we visualized microarray data of hepatocellular carcinoma (HCC) by using selforganizing-map, and investigated molecular signature representing the development of HCC. In this study, we propose two visualization methods of microarray data with Euclidean distance classifiers and Sammon’s nonlinear mapping. Our proposed methods will serve as tool to discover molecular signature representing the development of HCC for molecular biologists or doctors.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Takanobu Miyamoto, Yusuke Fujita, Shunji Uchimura, Yoshihiko Hamamoto, Norio Iizuka, Masaaki Oka
• Keywords: Computer Vision, Hepatocellular Carcinoma, ICPR 2008, Microarray Data, Molecular Signature