論文（英文）

◼︎査読付き論文

• Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida, Scaling law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions. npj Computational Materials 11: 146 (2025). DOI: 10.1038/s41524-025-01606-5
• Noda, K., Wakiuchi, A., Hayashi, Y., Yoshida,R., Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training. Communications Materials 6: 36 (2025). DOI: 10.1038/s43246-025-00754-x
• Maeda, H., Liang, Y., Hosoya, R., Marui, R., Yoshida, E., Chen, Y., Hatakeyama-Sato, K., Nabae, Y., Nakagawa, S., Morikawa, J., Tokita, M., Sawada, R., Ando, S., Hayashi, Y., Yoshida, R., Furuya, H., Hayakawa, T., Smectic liquid crystalline poly(ester imide)s with low dielectric dissipation factors for high-frequency applications. Polymer Journal (2025). DOI: 10.1038/s41428-025-01020-0
• Nanjo, S., Arifin, Maeda, H., Hayashi, Y., Hatakeyama-Sato, K., Himeno, R., Hayakawa, T., Yoshida, R., SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines. npj Computational Materials 11: 16 (2025). DOI: 10.1038/s41524-024-01492-3

◼︎プレプリント

• Kusaba, M., Iwayama, M., Yoshida, R., Bayesian Kernel Regression for Functional Data. arXiv: 2503.13676 (2025). DOI: 10.48550/arXiv.2503.13676

◼︎査読付き論文

• Liu, C., Tamaki, H., Yokoyama, T., Wakasugi, K., Yotsuhashi, S., Kusaba, M., Oganov, A. R., Yoshida, R., Shotgun crystal structure prediction using machine-learned formation energies. npj Computational Materials 10: 298 (2024). DOI: 10.1038/s41524-024-01471-8
• Fujita, E., Liu, C., Ishikawa, A., Mato, T., Kitahara, K., Tamura, R., Kimura, K., Yoshida, R., Katsura, Y., Comprehensive experimental datasets of quasicrystals and their approximants. Scientific Data 11: 1211 (2024). DOI: 10.1038/s41597-024-04043-z
• Arellano, F. J., Kusaba, M., Wu, S., Yoshida, R., Donkó, Z., Hartmann, Tsankov, T. V., Hamaguchi, S., Machine learning-based prediction of the electron energy distribution function and electron density of argon plasma from the optical emission spectra. Journal of Vacuum Science and Technology A 42: 053001 (2024). DOI: 10.1116/6.0003731
• Toyoshima, Y., Sato, H., Nagata, D., Kanamori, M., Jang, M. S., Kuze, K., Oe, S., Teramoto, T., Iwasaki, Y., Yoshida, R., Ishihara, T., Iino, Y., Ensemble dynamics and information flow deduction from whole-brain imaging data. PLoS Computational Biology 20(3): e1011848 (2024). DOI: 10.1371/journal.pcbi.1011848

◼︎プレプリント

• Maeda, H., Wu, S., Marui, R., Yoshida, E., Hatakeyama-Sato, K., Nabae, Y., Nakagawa, S., Ryu, M., Ishige, R., Noguchi, Y., Hayashi, Y., Ishii, M., Kuwajima, I., Jiang, F., Thang Vu, X., Ingebrandt, S., Tokita, M., Morikawa, J., Yoshida, R., Hayakawa, T., Discovery of liquid crystalline polymers with high thermal conductivity using machine learning. ChemRxiv (2024). DOI: 10.26434/chemrxiv-2024-tj786
• Nanjo, S., Arifin, Maeda, H., Hayashi, Y., Hatakeyama-Sato, K., Himeno, R., Hayakawa, T., Yoshida, R., SPACIER: On-Demand Polymer Design with Fully Automated All-Atom Classical Molecular Dynamics Integrated into Machine Learning Pipelines. arXiv (2024). DOI: 10.48550/arXiv.2408.05135

◼︎査読付き論文

• Minami, S., Fukumizu, K., Hayashi, Y., Yoshida, R., Transfer learning with affine model transformation. Advances in Neural Information Processing Systems 36 (2023). https://papers.nips.cc/paper_files/paper/2023/hash/3819a070922cc0d19f3d66ce108f28e0-Abstract-Conference.html
• Uryu, H., Yamada, T., Kitahara, K., Singh, A., Iwasaki, Y., Kimura, K., Miyao, N., Ishikawa, A., Tamura, R., Ohhashi, S., Liu, C., Yoshida, R., Deep learning enables rapid identification of a new quasicrystal from multiphase powder diffraction patterns. Advanced Science 11(1): 2304546 (2023). DOI: 10.1002/advs.202304546
• Kusaba, M., Hayashi, Y., Liu, C., Wakiuchi, A., Yoshida, R., Representation of materials by kernel mean embedding. Physical Review B 108: 134107 (2023). DOI: 10.1103/PhysRevB.108.134107
• Liu, C., Kitahara, K., Ishikawa, A., Hiroto, T., Singh, A., Fujita, E., Katsura, Y., Inada, Y., Tamura, R., Kimura, K., Yoshida, R., Quasicrystals predicted and discovered by machine learning. Physical Review Materials 7: 093805 (2023). DOI: 10.1103/PhysRevMaterials.7.093805
• Ohno, M., Hayashi, Y., Zhang, Q., Kaneko, Y., Yoshida, R., SMiPoly: Generation of a synthesizable polymer virtual library using rule-based polymerization reactions. Journal of Chemical Information and Modeling 63(17): 5539-5548 (2023). DOI: 10.1021/acs.jcim.3c00329
• Aoki, Y., Wu, S., Tsurimoto, T., Hayashi, Y., Minami, S., Tadamichi, O., Shiratori, K., Yoshida, R., Multitask machine learning to predict polymer–solvent miscibility using flory–huggins interaction parameters. Macromolecules 56(14): 5446–5456 (2023). DOI: 10.1021/acs.macromol.2c02600
• Zhang, Q., Liu, C., Wu, S., Hayashi, Y., Yoshida, R., A Bayesian method for concurrently designing molecules and synthetic reaction networks. Science and Technology of Advanced Materials: Methods 3(1): 2204994 (2023). DOI: 10.1080/27660400.2023.2204994
• Zamengo, M., Wu, S., Yoshida, R., Morikawa, J., Multi-objective optimization for assisting the design of fixed-type packed bed reactors for chemical heat storage. Applied Thermal Engineering 218: 119327 (2023). DOI: 10.1016/j.applthermaleng.2022.119327

◼︎プレプリント

• Liu, C., Tamaki, H., Yokoyama, T., Wakasugi, K., Yotsuhashi, S., Yoshida, R., Shotgun crystal structure prediction using machine-learned formation energies. arXiv (2023). DOI: 10.48550/arXiv.2305.02158

◼︎査読付き論文

• Hayashi, Y., Shiomi, J., Morikawa, J., Yoshida, R., RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics. npj Computational Materials 8: 222 (2022). DOI: 10.1038/s41524-022-00906-4
• Ma, R., Zhang, H., Xu, J., Sun, L., Hayashi, Y., Yoshida, R., Shiomi, J., Wang, J-X., Luo, T., Machine learning-assisted exploration of thermally conductive polymers based on high-throughput molecular dynamics simulations. Materials Today Physics 28: 100850 (2022). DOI: 10.1016/j.mtphys.2022.100850
• Kusaba, M., Liu, C., Yoshida, R., Crystal structure prediction with machine learning-based element substitution. Computational Materials Science 211: 111496 (2022). DOI: 10.1016/j.commatsci.2022.111496
• Iwayama, M., Wu, S., Liu, C., Yoshida, R., Functional output regression for machine learning in materials science. Journal of Chemical Information and Modeling 62: 4837–4851 (2022). DOI: 10.1021/acs.jcim.2c00626
• Torres, P., Wu, S., Ju, S., Liu, C., Tadano, T., Yoshida, R., Shiomi, J., Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach. Journal of Physics: Condensed Matter 34(13) (2022). DOI: 10.1088/1361-648X/ac49c9

• Liu, C., Fujita, E., Katsura, Y., Inada, Y., Ishikawa, A., Tamura, R., Kimura, K., Yoshida, R., Machine learning to predict quasicrystals from chemical compositions. Advanced Materials 33(36) (2021). DOI: 10.1002/adma.202102507
• Minami, S., Liu, S., Wu, S., Fukumizu, K., Yoshida, R., A general class of transfer learning regression without implementation cost. Proceedings of the AAAI Conference on Artificial Intelligence 35(10): 8992-8999 (2021). DOI: 10.1609/aaai.v35i10.17087
• Ju, S., Yoshida, R., Liu, C., Wu, S., Hongo, K., Tadano, T., Shiomi, J., Exploring diamondlike lattice thermal conductivity crystals via feature-based transfer learning. Physical Review Materials 5: 053801 (2021). DOI: 10.1103/PhysRevMaterials.5.053801
• Kusaba, M., Liu, C., Koyama, Y., Terakura, K., Yoshida, R., Recreation of the periodic table with an unsupervised machine learning algorithm. Scientific Reports 11: 4780 (2021). DOI: 10.1038/s41598-021-81850-z

• Wu, S., Yamada, H., Hayashi, Y., Zamengo, M., Yoshida, R., Potentials and challenges of polymer informatics: exploiting, machine learning for polymer design. arXiv preprint. arXiv:2010.07683 (2020). DOI: 10.48550/arXiv.2010.07683
• Guo, Z., Wu, S., Ono, M., Yoshida, R., Bayesian algorithm for retrosynthesis. Journal of Chemical Information and Modeling 60(10): 4474-4486 (2020). DOI: 10.1021/acs.jcim.0c00320
• Toyoshima, Y., Wu, S., Kanamori, M., Sato, H., Jang, M. S., Oe, S., Murakami, Y., Teramoto, T., Park, C., Iwasaki, Y., Ishihara, T., Yoshida, R., Iino, Y., Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans. BMC Biology 18(1): 1-20 (2020). DOI: 10.1186/s12915-020-0745-2

• Wu, S., Lambard, G., Liu, C., Yamada, H., Yoshida, R., iQSPR in XenonPy: A Bayesian molecular design algorithm. Molecular Informatics 39(1-2): 1900107 (2019). DOI: 10.1002/minf.201900107
• Yamada, H., Liu, C., Wu, S., Koyama, Y., Ju, S., Shiomi, J., Morikawa, J., Yoshida, R., Predicting materials properties with little data using shotgun transfer learning. ACS Central Science 5(10): 1717-1730 (2019). DOI: 10.1021/acscentsci.9b00804
• Takubo, N., Yura, F., Naemura, K., Yoshida, R., Tokunaga, T., Tokihiro, T., Kurihara, H., Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis. Scientific Reports 9: 9304 (2019). DOI: 10.1038/s41598-019-45666-2
• Wu, S., Kondo, Y., Kakimoto, M., Yang, B., Yamada, H., Kuwajima, I., Lambard, G., Hongo, K., Xu, Y., Shiomi, J., Schick, C., Morikawa, J., Yoshida, R., Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Computational Materials 5: 66 (2019). DOI: 10.1038/s41524-019-0203-2
• N. Takubo, F. Yura, K. Naemura, R. Yoshida, T. Tokunaga, T. Tokihiro, H. Kurihara, Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis. Scientific Reports 9: 9304 (2019). DOI: 10.1038/s41598-019-45666-2

• Kawamura, Y., Koyama, S., Yoshida, R., Statistical inference of the rate of RNA polymerase II elongation by total RNA sequencing. Bioinformatics 35(11): 1877–1884 (2019). DOI: 10.1093/bioinformatics/bty886

• Ikebata, H., Hongo, K., Isomura, T., Maezono, R., Yoshida, R., Bayesian molecular design with a chemical language model. Journal of Computer-Aided Molecular Design 31(4): 379-391 (2017). DOI: 10.1007/s10822-016-0008-z [PubMed] [Software]
• Hirose, O., Kawaguchi, S., Tokunaga, T., Toyoshima, Y., Teramoto, T., Kuge, S., Ishihara, T., Iino, Y., Yoshida, R., SPF-CellTracker: Tracking multiple cells with strongly-correlated moves using a spatial particle filter. IEEE/ACM Transactions on Computational Biology and Bioinformatics 15(6): 1822-1831 (2017). DOI: 10.1109/TCBB.2017.2782255 [IEEE Xplore] [Software]

• Nakata, A., Yoshida, R., Yamaguchi, R., Yamauchi, M., Tamada, Y., Fujita, A., Shimamura, T., Imoto, S., Higuchi, T., Nomura, M., Kimura, T., Nokihara, H., Higashiyama, M., Kondoh, K., Nishihara, H., Tojo, A., Yano, S., Miyano, S., Gotoh, N., Elevated β-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs, Scientific Reports 5: 13706 (2015). [PubMed] [Scientific Reports]
• Ikebata, H., Yoshida, R. Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets, Bioinformatics 31(10): 1561-1568 (2015). [PubMed] [Bioinformatics] [Software]

• Yamashita, H., Higuchi, T., Yoshida, R., Atom environment kernels on molecules, Journal of Chemical Information and Modeling 54(5): 1289–1300 (2014). [PubMed] [ACS Pubilcations]
• Tokunaga, T., Hirose, O., Kawaguchi, S., Toyoshima, Y., Teramoto, T., Ikebata, H., Kuge, S., Ishihara, T., Iino, Y., Yoshida, R., Automated detection and tracking of many cells by using 4D live-cell imaging data, Bioinformatics 30(12): i43-i51 (2014). [PubMed] [Bioinformatics]

• Tokunaga, T., Yoshida, R., Iwasaki, Y., Data assimilation for reconstructing a whole neuronal system of C. Elegans – The current state and issue, Journal of The Japan Society for Simulation Technology 3(4): 287-294 (2013). (in Japanese)

• Yamauchi, M., Yamaguchi, R., Nakata, A., Kohno, T., Nagasaki, M., Shimamura, T., Imoto, S., Saito, A., Ueno, K., Hatanaka, Y., Yoshida, R., Higuchi, T., Nomura, M., Beer, D. G., Yokota, J., Miyano, S., Gotoh, N., Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma, PLoS One 7(9): e43923 (2012). [PubMed] [PLoS One]
• Kawano, S., Shimamura, T., Niida, A., Imoto, S., Yamaguchi, R., Nagasaki, M., Yoshida, R., Print, C., Miyano , S., Identifying gene pathways associated with cancer characteristics via sparse statistical methods, IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(4): 966-972 (2012). [PubMed]

• Tamada, Y., Yamaguchi, R., Imoto, S., Hirose, O., Yoshida, R., Nagasaki, M., Miyano, S., SiGN-SSM: open source parallel software for estimating gene networks with state space models, Bioinformatics 27(8): 1172-1173 (2011). [PubMed] [Bioinformatics] [Software]

• Kawano, S., Shimamura, T., Niida, A., Imoto, S., Yamaguchi, R., Nagasaki, M., Yoshida, R., Print, C., Miyano , S., Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning, Proc. IEEE Bioinformatics and Biomedicine 253-258 (2010). (BIBM2010: Refereed conference. 61 papers are accepted as regular papers from 355 submissions (acceptance rate 17.2%)) [IEEE Xplore]
• Yoshida, R., Saito, M., Nagao, H., Higuchi T., Bayesian experts i exploring reaction kinetics of transcription circuits, Bioinformatics 26(18): i589-595 (2010). [PubMed] [Bioinformatics] [Supplementary Information]
• Yoshida, R., West, M., Bayesian learning in sparse graphical facto models via variational mean-field annealing, Journal of Machine Learning Research 11: 1771-1798 (2010). [Software][Supporting Information]
• Hayashi, K., Saito, M., Yoshida, R., Higuchi, T., Implementation of sequential importance sampling in GPGPU, Proceedings of the 13th International Conference on Information Fusion: 1-6 (2010). [IEEE Xplore]

• Kojima, K., Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Ueno, K., Higuchi, T., Gotoh, N., Miyano, S., A state space representation of VAR models with sparse learning for dynamic gene networks, Genome Informatics 22: 56-68, 227-238 (2009). [PubMed] [IBSB2009 on line]
• Yoshida, R., Higuchi, T., Graphical modeling of intercellular biochemical pathways and statistical inference, Journal of the Japan Statistical Society, Bioinformatics special edition 38(2): 213-236 (2009). (in Japanese) [CiNii]
• Nakamura, K., Yoshida, R., Nagasaki, M., Miyano, S., Higuchi, T., Parameter estimation of in silico biological pathways with particle filtering towards a petascale computing,Pacific Symposium on Biocomputing 227-238 (2009). [PubMed] [PSB on line]
• Yoshida, R., Nagasaki, M., Yamaguchi, R., Imoto, S., Miyano, S., Higuchi, T., Bayesian learning of biological pathways on genomic data assimilation, Bioinformatics 24(22): 2592-2601 (2008). [PubMed] [Bioinformatics][Software]
• Numata, K., Yoshida, R., Nagasaki, M., Saito, A., Imoto, S., Miyano, S., ExonMiner: Web service for analysis of GeneChip exon array data,BMC bioinformatics 9(1): 494 (2008). [PubMed] [BMC Bioinformatics] [Software]
• Yamaguchi, R., Imoto, S., Yamauchi, M., Nagasaki, M., Yoshida, R., Shimamura, T., Hatanaka, Y., Ueno, K., Higuchi, T., Gotoh, N., Miyano, S., Predicting differences in gene regulatory systems by state space models, Genome Informatics 21: 101-113 (2008). Finalist Best Paper Award by 19th International Conference on Genome Informatics (GIW2008) [PubMed]
• Hirose, O., Yoshida, R., Yamaguchi, R., Imoto, S., Higuchi, T., Miyano, S., Analyzing time course gene expression data with biological and technical replicates to estimate gene networks by state space models, Proc. 2nd Asia International Conference on Modelling & Simulation 940-946 (2008). (AMS2008: Refereed conference) [IEEE Xplore]
• Hirose, O.*, Yoshida, R.*, Imoto, S., Yamaguchi, R., Higuchi, T., Stephen Charnock-Jones, D., Print, C., Miyano, S., Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models, Bioinformatics 24(7): 932-42 (2008). (* These authors equally contributed this work) [PubMed] [Bioinformatics] [Software]
• Yoshida, R., Numata, K., Imoto, S., Nagasaki, M., Doi, A., Ueno, K., Miyano, S., Computational genome-wide discovery of aberrant splice variations with exon expression profiles, Proc. IEEE 7th International Symposium on Bioinformatics &Bioengineering 715-722 (2007). [Supplementary Information] [IEEE Xplore]
• Hirose, O., Yoshida, R., Yamaguchi, R., Imoto, S., Higuchi, T., Miyano, S., Clustering with time course gene expression profiles and the mixture of state space models, Genome Informatics 18:258-266 (2007). [PubMed] [World Scientific]
• Yamaguchi, R., Yamamoto, M., Imoto, S., Nagasaki, M., Yoshida, R., Tsuji, K., Ishige, A., Asou, H., Watanabe, K., Miyano, S., Identification of activated transcription factors from microarray gene expression data of Kampo-medicine treated mice, Genome Informatics 18: 119-129 (2007). [PubMed] [World Scientific
• Henmi, M., Yoshida, R., Eguchi, S., Importance sampling with the estimated sampler, Biometrika 94(4): 985-991 (2007). [Biometrika]
• Gupta, P.K.*, Yoshida, R.*, Imoto, S., Yamaguchi, R., Miyano, S., Statistical absolute evaluation of gene ontology terms with gene expression data, Proc. 3rd International Symposium on Bioinformatics Research and Applications , Lecture Note in Bioinformatics, Springer-Verlag 4463: 146-157 (2007). (ISBRA2007: Refereed conference) (* These authors equally contributed this work) [SpringerLink]
• Yamaguchi, R., Yoshida, R., Imoto, S., Higuchi, T., Miyano S., Finding module-based gene networks in time-course gene expression data with state space models, IEEE Signal Processing Magazine 24(1): 37-46 (2007). [IEEE Xplore]
• Tasaki, S., Nagasaki, M., Oyama, M., Hata, H., Ueno, K., Yoshida, R., Higuchi, T., Sugano, S., Miyano, S., Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data, Genome Informatics 17(2): 226-238 (2006).[PubMed] [JSBI]
• Yoshida, R.*, Numata, K.*, Imoto, S., Nagasaki, M., Doi, A., Ueno, K., Miyano, S., A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST arrays, Genome Informatics 17(1): 88-99 (2006). (* These authors equally contributed this work)[PubMed] [JSBI] [Supplementary Information]
• Nagasaki, M., Yamaguchi, R., Yoshida, R., Imoto, S., Doi, A., Tamada, Y., Matsuno, H., Miyano, S., Higuchi , T., Genomic data assimilation for estimating Hybrid Functional Petri Net from time-course gene expression data, Genome Informatics 17(1): 46-61 (2006). [PubMed] [JSBI]
• Yoshida, R., Higuchi, T., Imoto, S., Miyano, S., ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles, Bioinformatics 22(12): 1538-1539 (2006). [PubMed] [Bioinformatics] [Software]
• Yoshida, R., Higuchi, T., Imoto, S., Estimating time-dependent gene networks from time series DNA microarray data by dynamic linear model with Markov switching, Proc. IEEE 4th Computational Systems Bioinformatics (CSB2005: Refereed Conference) 289-298 (2005).[PubMed] [CSB2005]
• Yoshida, R., Imoto, S., Higuchi, T., A penalized likelihood estimation on transcriptional module-based clustering, Proc. 1st International Workshop on Data Mining and Bioinformatics (DMBIO 2005: Refereed Conference), Lecture Note in Computer Science, 3482, Springer-Verlag, 389-401 (2005). [SpringerLink]
• Yoshida, R., Method for approximating target distribution of Importance Sampling, Journal of the Statistical Society Japanese Issue 34: 21-37 (2004).
• Yoshida, R., Higuchi, T., Imoto, S., A mixed factors model for dimension reduction and extraction of a group structure in gene expression data, Proc. IEEE 3rd Computational Systems Bioinformatics (CSB2004: Refereed Conference) 161-172 (2004). [PubMed] [CSB2004]