Kyushu University Academic Staff Educational and Research Activities Database
List of Presentations
Shinsuke Uda Last modified date:2023.06.19

Associate Professor / Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation / Medical Research Center for High Depth Omics / Medical Institute of Bioregulation

1. Matsuzaki F., Uda S., Yamauchi Y., Kubota H., Trans-omic analysis of insulin response in the liver, International Symposium on Evolutionary Genomics and Bioinformatics 2022 (ISEGB2022), 2022.03.
2. Yuki Ito, Shinsuke Uda, Toshiya Kokaji, Akiyoshi Hirayama, Tomoyoshi Soga, Yutaka Suzuki, Shinya Kuroda and Hiroyuki Kubota, Comparison of hepatic responses to glucose perturbation between normal and obese mice using edge ontology, Fusion of Mathematics and Biology, 2020.10.
3. Shinsuke Uda, Approximate Conditional Independence Test using Residuals, 12th International Conference on Agents and Artificial Intelligence, 2020.02.
4. Shinsuke Uda, Network structure inference by conditional independence, 第57回日本生物物理学会年会, 2019.09.
5. Katsumi Konishi, Masashi Fujii, Katsuyuki Kunida, Shinsuke Uda, Shinya Kuroda, Matrix rank minimization approach to signal recovery and nonlinear function estimation for nonlinear ARX model with input nonlinearity, 2018.02, This paper deals with an input/output signal recovery problem for nonlinear multiple-input single-output autoregressive exogenous (ARX) models with input nonlinearity, which are used in data-driven systems biology. A matrix rank minimization approach is applied, and a new signal recovery algorithm for nonlinear ARX models is provided. The proposed algorithm recovers output signals and nonlinear-transformed input signals on a linear subspace using some assumptions about nonlinear functions and does not require the exact knowledge of nonlinear functions. Numerical examples using experimental data of signal transduction of cellular systems show the efficiency of the proposed algorithm..
6. Shinsuke Uda, Hiroyuki Kubota, Sparse Gaussian graphical model with missing values, 21st Conference of Open Innovations Association, FRUCT 2017, 2018.01, Recent advances in measurement technology have enabled us to measure various omic layers, such as genome, transcriptome, proteome, and metabolome layers. The demand for data analysis to determine the network structure of the interaction between molecular species is increasing. The Gaussian graphical model is one method of estimating the network structure. However, biological omics data sets tend to include missing values, which is conventionally handled by preprocessing. We propose a novel method by which to estimate the network structure together with missing values by combining a sparse graphical model and matrix factorization. The proposed method was validated by artificial data sets and was applied to a signal transduction data set as a test run..
7. Shinsuke Uda, Bayesian common parameter model in systems biology, International Meeting on “High-Dimensional Data-Driven Science” (HD3-2017), 2017.09.
8. 宇田 新介, 久保田 浩行, An estimation method of sparse partial correlation matrix for omics data analysis, 11th International Symposium of The Institute Network “Frontiers in Biomedical Sciences”, 2017.01.
9. 宇田 新介, 黒田 真也, Information theoretical analysis of signaling pathways, the 6th Annual World Congress of Molecular & Cell Biology, 2016.04.
10. 宇田 新介, Estimation method of sparse partial correlation matrix from omics data set with missing values, International Meeting on “High-Dimensional Data Driven Science” (HD3-2015), 2015.12.
11. Katsumi Konishi, Takaho Tsuchiya, Katsuyuki Kunida, Masashi Fujii, 宇田 新介, Shinya Kuroda, Multiple low rank matrices approach to piecewise affine system identification for nonuniform sampling data, The 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications, 2015.12.
12. Kentaro Kawata, Katsuyuki Yugi, Atsushi Hatano, Katsuyuki Kunida, Masashi Fujii, Satoshi Ohno, Yoko Tomizawa, Takanori Sano, Hiroaki Kakuda, 宇田 新介, 久保田 浩行, Yutaka Suzuki, 松本 雅記, 中山 敬一, Shinya Kuroda, Reconstruction of global network of insulin-dependent gene expression based on phosphoproteome and transcriptome data, International Conference on Systems Biology(ICSB2015), 2015.11.
13. Shinsuke Uda, Shinya Kuroda, Robustness and compensation of information transmission of signaling pathways despite perturbation, the Joint Annual Meeting of the Japanese Society for Mathematical Biology and the Society for Mathematical Biology, 2014.07.
14. Shinsuke Uda, Shinya Kuroda, Analysis of ERK pathway in PC12 cells from information theoretical approach, The 33rd annul meeting of the molecular biology society of Japan, 2010.12.
15. Shinsuke Uda, Shinya Kuroda, Information theoretic analysis of ERK pathway in PC12 cell, The 11th International Conference on Systems Biology ICSB2010, 2010.10.
16. Yoshiyuki Kabashima, Shinsuke Uda, A BP-Based Algorithm for Performing Bayesian Inference in Large Perceptron-Type Networks, 2004.