Maeda, I., Hasegawa, K., Kaneko, H., Funatsu, K. Novel Method Proposing Chemical Structures with Desirable Profile of Activities Based on Chemical and Protein Spaces. Molecular Informatics, 36(12) (2017)
Conferences
Maeda, I., Kaneko, H., Funatsu, K. Visualization of chemical space and protein space considering compound-protein interaction, In: The 44th Symposium on Structure-Activity Relationship, pp. 25-28 (2016)
Maeda, I., Matsushima, H., Sakaji, H., Izumi, K., deGraw, D., Tomioka, H., Kato, A., Short-time market trend prediction by considering time series of high frequency order. In: SigFin21, (2018)
Maeda, I., Matsushima, H., Sakaji, H., Izumi, K., deGraw, D., Tomioka, H., Kato, A., Kitano, M., Learning Uncertainty in Market Trend Forecast Using Bayesian Neural Networks. In: International Conference on Decision Economics (DECON), (2019)
Maeda, I., Matsushima, H., Sakaji, H., Izumi, K., deGraw, D., Tomioka, H., Kato, A., Kitano, Importance of Uncertainty Estimation in Deep Learning. In: The 33rd Annual Conference of the Japanese Society for Artificial Intelligence , (2019)
3. Announcement
Maeda, I., Yuge, N., Watanabe, T., Sugawara, Y., Miuchi, N., 実測値の不確かさを考慮した活性予測モデル構築によるSirtuin1 阻害剤探索. In: The 4th AIDD contest, (2017)
Indo, S., Maeda, I., Kitagawa, K., Fujita, K., Shimono, T., Deep Quoridor. In: Deep Learning Day, (2017)
4. Others
SAR Presentation Award In: 44th Symposium on Structure-Activity Relationship, (2016)