Article-English

2024

■Peer reviewed papers

  • 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). https://doi.org/10.1371/journal.pcbi.1011848

2023

■Peer reviewed papers

  • 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: 2304546 (2023). DOI: https://doi.org/10.1002/advs.202304546
  • Kusaba, M., Hayashi, Y., Liu, C., Wakiuchi, A., Yoshida, R., Representation of materials by kernel mean embedding. Physcal Review B 108: 134107 (2023). DOI: https://doi.org/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. Physcal Review Materials 7: 093805 (2023). DOI: https://doi.org/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 (2023). DOI: https://doi.org/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: https://doi.org/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: https://doi.org/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: https://doi.org/10.1016/j.applthermaleng.2022.119327

◼︎Preprint

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

2022

■Peer reviewed papers

  • 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: https://doi.org/10.1038/s41524-022-00906-4
  • Ma, R., Zhang, H., Xu, J., Sun, L., Hayashi, Y., Yoshida, Y., 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: https://doi.org/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: https://doi.org/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: https://doi.org/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: https://doi.org/10.1088/1361-648X/ac49c9

2021

  • 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: https://doi.org/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: https://ojs.aaai.org/index.php/AAAI/article/view/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: https://doi.org/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: https://doi.org/10.1038/s41598-021-81850-z

2020

  • 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). arXiv:2010.07683v1
  • Guo, Z., Wu, S., Ono, M., Yoshida, R., Bayesian algorithm for retrosynthesis. Journal of Chemical Information and Modeling 60(10): 4474-4486 (2020). DOI: https://doi.org/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: https://doi.org/10.1186/s12915-020-0745-2

2019

  • 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: https://doi.org/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: https://doi.org/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: https://doi.org/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: https://doi.org/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: https://doi.org/10.1038/s41598-019-45666-2

2018

  • 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: https://doi.org/10.1093/bioinformatics/bty886

2017

  • 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: https://doi.org/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: https://doi.org/10.1109/TCBB.2017.2782255 [IEEE Xplore] [Software]

2016

  • Toyoshima, Y., Tokunaga, T., Hirose, O., Kanamori, M., Teramoto, T., Jang, MS., Kuge, S., Ishihara, T., Yoshida, R., Iino, Y., Accurate automatic detection of densely distributed cell nuclei in 3D space, PLoS Computational Biology 12(6): e1004970 (2016).[PubMed][PLoS Computational Biology]

2015

  • 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]

2014

  • 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]

2013

  • 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)

2012

  • 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]

2011

  • 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]

2010

  • 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]

2009

  • 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]