We use information science technologies to predict and prevent the occurrence and epidemic spread of zoonosis. Through efforts combining informatics and life science, we are conducting comprehensive analyses on a large number of influenza viruses that circulated in the past, and thus develop methods to predict the virus's evolution into the future. In this program, we are responsible for teaching information science to graduate students. By introducing computational methodologies for data analysis and biostatistics into veterinary science education, we will produce experts who have global perspectives and professional judgment.

1. Teaching information science to graduate students
2. Support for graduate students in this program
3. Educational affairs
4. Publicity activity for the program

1. Ito, K., Igarashi, M., Miyazaki, Y., Murakami, T., Iida, S., Kida, H., Takada, A., 2011. Gnarled-trunk evolutionary model of influenza A virus hemagglutinin. PloS one, 6(10): e25953.
2. Ito, K., Zeugmann, T., Zhu, Y., 2010. Clustering the Normalized Compression Distance for Influenza Virus Data. Lect. Notes Comput. Sc., 6060:130-146.
3. Igarashi, M., Ito, K., Yoshida, R., Tomabechi, D., Kida, H., Takada, A., 2010. Predicting the antigenic structure of the pandemic (H1N1) 2009 influenza virus hemagglutinin. PloS one, 5(1): e8553-e8553.
4. Sata, K., Hirata, K., Ito, K., Kuboyama, T., 2009. Discovering Networks for Global Propagation of Influenza A (H3N2) Viruses by Clustering. Lect. Notes Comput. Sc., 5712:490-497.
5. Igarashi, M., Ito, K., Kida, H., Takada, A., 2008. Genetically destined potentials for N-linked glycosylation of influenza virus hemagglutinin. Virology, 376(2): 323-329.