統計数理研究所吉田亮研究室

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. (2021).
DOI: https://doi.org/10.1002/adma.202102507

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

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

2020

C. Liu, E. Fujita, Y. Katsura, Y. Inada, A. Ishikawa, R. Tamura, K. Kimura, R. Yoshida (2021) Machine learning to predict quasicrystals from chemical compositions, Nature Portfolio, rs-240290/v1
[Nature Portfolio] DOI:10.21203/rs.3.rs-240290/v1

S. Minami, S. Liu, S. Wu, K. Fukumizu, R. Yoshida (2020) A general class of transfer learning regression without implementation cost, In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI) 2021, in press.

Z. Guo, S. Wu, M. Ohno, R. Yoshida (2020) Bayesian Algorithm for Retrosynthesis, Journal of Chemical Information and Modeling, 60(10):4474–4486
[ACS Pubilcations] DOI:https://doi.org/10.1021/acs.jcim.0c00320

S. Minami, S. Liu, S. Wu, K. Fukumizu, R. Yoshida (2020) A general class of transfer learning regression without implementation cost, arXiv, arXiv:2006.13228.
[arXiv]

Y. Toyoshima, S. Wu, M. Kanamori, H. Sato, M. S. Jang, S. Oe, Y. Murakami, T. Teramoto, C. Park, Y. Iwasaki, T. Ishihara, R. Yoshida, Y. Iino (2020) Neuron ID dataset facilitates neuronal annotation for whole-brain activity imaging of C. elegans, BMC Biology, 18(1):1-20
[BMC Biology] DOI:https://doi.org/10.1186/s12915-020-0745-2

Z. Guo, S. Wu, M. Ohno, R. Yoshida (2020) A Bayesian algorithm for retrosynthesis, arXiv, arXiv:2003.03190.
[arXiv]

2019

M. Kusaba, C. Liu, Y. Koyama, K. Terakura, R. Yoshida (2019) Recreation of the periodic table with an unsupervised machine learning algorithm, arXiv, arXiv:1912.10708.
[arXiv]

S. Wu, G. Lambard, C. Liu, H. Yamada, R. Yoshida (2019) iQSPR in XenonPy: A Bayesian molecular design algorithm, Molecular Informatics, 39(1-2):1900107.
[Molecular Informatics] [PubMed] DOI:https://doi.org/10.1002/minf.201900107

S. Ju, R. Yoshida, C. Liu, K. Hongo, T. Tadano, J. Shiomi (2019) Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning, arXiv, arXiv:1909.11234.
[arXiv]

H. Yamada, C. Liu, S. Wu, Y. Koyama, S. Ju, J. Shiomi, J. Morikawa, R. Yoshida (2019) Predicting materials properties with little data using shotgun transfer learning, ACS Central Science, 5(10):1717–1730
[ACS Central Science] DOI:https://doi.org/10.1021/acscentsci.9b00804

Y. Toyoshima, S. Wu, M. Kanamori, H. Sato, M.S. Jang, S. Oe, Y. Murakami, T. Teramoto, C. Park, Y. Iwasaki, T. Ishihara, R. Yoshida, Y. Iino (2019) An annotation dataset facilitates automatic annotation of whole-brain activity imaging of C. elegans, bioRxiv, 18(1), 1-20.
[bioRxiv] DOI:https://doi.org/10.1101/698241

N. Takubo, F. Yura, K. Naemura, R. Yoshida, T. Tokunaga, T. Tokihiro, H. Kurihara (2019) Cohesive and anisotropic vascular endothelial cell motility driving angiogenic morphogenesis, Scientific Reports, 9:9304.
[Scientific Reports] DOI:https://doi.org/10.1038/s41598-019-45666-2

S. Wu, Y. Kondo, M. Kakimoto, B. Yang, H. Yamada, I. Kuwajima, G. Lambard, K. Hongo, Y. Xu, J. Shiomi, C. Schick, J. Morikawa, R. Yoshida (2019) Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm, npj Computational Materials, 5:66.
[npj Computational Materials] DOI:https://doi.org/10.1038/s41524-019-0203-2

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

2018

H. Ikebata, K. Hongo, T. Isomura, R. Maezono, R. Yoshida (2017) Bayesian molecular design with a chemical language model, Journal of Computer-Aided Molecular Design, 31(4):379-391.
[PubMed] [Springer] [Software]

2017

O. Hirose, S. Kawaguchi, T. Tokunaga, Y. Toyoshima, T. Teramoto, S. Kuge, T. Ishihara, Y. Iino, R. Yoshida (2017) 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).
[IEEE Xplore]

2016

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

2015

A. Nakata, R. Yoshida, R. Yamaguchi, M. Yamauchi, Y. Tamada, A. Fujita, T. Shimamura, S. Imoto, T. Higuchi, M. Nomura, T. Kimura, H. Nokihara, M. Higashiyama, K. Kondoh, H. Nishihara, A. Tojo, S. Yano, S. Miyano, N. Gotoh (2015) Elevated β-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs, Scientific Reports, 5:13706.
[PubMed][Scientific Reports]

H. Ikebata, R. Yoshida (2015) Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets, Bioinformatics, 31(10):1561-1568.
[PubMed][Bioinformatics][Software]

2014

H. Yamashita, T. Higuchi, R. Yoshida (2014) Atom environment kernels on molecules, Journal of Chemical Information and Modeling, 54(5):1289–1300.
[PubMed][ACS Pubilcations]

T. Tokunaga, O. Hirose, S. Kawaguchi, Y. Toyoshima, T. Teramoto, H. Ikebata, S. Kuge, T. Ishihara, Y. Iino, R. Yoshida (2014) Automated detection and tracking of many cells by using 4D live-cell imaging data, Bioinformatics, 30(12):i43-i51.
[PubMed][Bioinformatics]

2013

T. Tokunaga, R. Yoshida, Y. Iwasaki (2013) 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. (in Japanese)

2012

M. Yamauchi, R. Yamaguchi, A. Nakata, T. Kohno, M. Nagasaki, T. Shimamura, S. Imoto, A. Saito, K. Ueno, Y. Hatanaka, R. Yoshida, T. Higuchi, M. Nomura, D. G. Beer, J. Yokota, S. Miyano, N. Gotoh (2012) Epidermal growth factor receptor tyrosine kinase defines critical prognostic genes of stage I lung adenocarcinoma, PLoS One, 7(9):e43923. [PubMed][PLoS One]

S. Kawano, T. Shimamura, A. Niida, S. Imoto, R. Yamaguchi, M. Nagasaki, R. Yoshida, C. Print, S. Miyano (2012) Identifying gene pathways associated with cancer characteristics via sparse statistical methods, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(4):966-972. [PubMed]

2011

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

2010

S. Kawano, T. Shimamura, A. Niida, S. Imoto, R. Yamaguchi, M. Nagasaki, R. Yoshida, C. Print, S. Miyano (2010) Discovering functional gene pathways associated with cancer heterogeneity via sparse supervised learning, Proc. IEEE Bioinformatics and Biomedicine, 253-258. (BIBM2010: Refereed conference. 61 papers are accepted as regular papers from 355 submissions (acceptance rate 17.2%)) [IEEE Xplore]

R. Yoshida, M. Saito, H. Nagao, T. Higuchi (2010) Bayesian experts in exploring reaction kinetics of transcription circuits, Bioinformatics, 26(18):i589-595.
[PubMed][Bioinformatics][Supplementary Information]

R. Yoshida, M. West (2010) Bayesian learning in sparse graphical factor models via variational mean-field annealing, Journal of Machine Learning Research, 11:1771-1798.
[JMLR online][Software][Supporting Information]

K. Hayashi, M. Saito, R. Yoshida, T. Higuchi (2010) Implementation of sequential importance sampling in GPGPU, Proceedings of the 13th International Conference on Information Fusion, 1-6. [IEEE Xplore]

2009

K. Kojima, R. Yamaguchi, S. Imoto, M. Yamauchi, M. Nagasaki, R. Yoshida, T. Shimamura, K. Ueno, T. Higuchi, N. Gotoh, S. Miyano (2009) A state space representation of VAR models with sparse learning for dynamic gene networks, Genome Informatics, 22:56-68, 227-238. [PubMed][IBSB2009 on line]

R. Yoshida, T. Higuchi (2009) Graphical modeling of intercellular biochemical pathways and statistical inference, Journal of the Japan Statistical Society, Bioinformatics special edition, 38(2):213-236. (in Japanese) [CiNii]

K. Nakamura, R. Yoshida, M. Nagasaki, S. Miyano, T. Higuchi (2009) Parameter estimation of in silico biological pathways with particle filtering towards a petascale computing, Pacific Symposium on Biocomputing, 227-238. [PubMed][PSB on line]

R. Yoshida, M. Nagasaki, R. Yamaguchi, S. Imoto, S. Miyano, T. Higuchi (2008) Bayesian learning of biological pathways on genomic data assimilation, Bioinformatics, 24(22):2592-2601. [PubMed][Bioinformatics][Software]

K. Numata, R. Yoshida, M. Nagasaki, A. Saito, S. Imoto, S. Miyano (2008) ExonMiner: Web service for analysis of GeneChip exon array data, BMC bioinformatics, 9(1):494. [PubMed][BMC Bioinformatics][Software]

R. Yamaguchi, S. Imoto, M. Yamauchi, M. Nagasaki, R. Yoshida, T. Shimamura, Y. Hatanaka, K. Ueno, T. Higuchi, N. Gotoh, S. Miyano (2008) Predicting differences in gene regulatory systems by state space models, Genome Informatics, 21:101-113. Finalist Best Paper Award by 19th International Conference on Genome Informatics (GIW2008)
[PubMed]

O. Hirose, R. Yoshida, R. Yamaguchi, S. Imoto, T. Higuchi, S. Miyano (2008) 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. (AMS2008: Refereed conference)
[IEEE Xplore]

O. Hirose*, R. Yoshida*, S. Imoto, R. Yamaguchi, T. Higuchi, D. Stephen Charnock-Jones, C. Print, S. Miyano (2008) Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models, Bioinformatics, 24(7):932-42. (* These authors equally contributed this work)
[PubMed][Bioinformatics][Software]

R. Yoshida, K. Numata, S. Imoto, M. Nagasaki, A. Doi, K. Ueno, S. Miyano (2007) Computational genome-wide discovery of aberrant splice variations with exon expression profiles, Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, 715-722. [Supplementary Information][IEEE Xplore]

O. Hirose, R. Yoshida, R. Yamaguchi, S. Imoto, T. Higuchi, S. Miyano (2007) Clustering with time course gene expression profiles and the mixture of state space models, Genome Informatics, 18:258-266. [PubMed][World Scientific]

R. Yamaguchi, M. Yamamoto, S. Imoto, M. Nagasaki, R. Yoshida, K. Tsuji, A. Ishige, H. Asou, K. Watanabe, S. Miyano (2007) Identification of activated transcription factors from microarray gene expression data of Kampo-medicine treated mice, Genome Informatics, 18:119-129. [PubMed][World Scientific]

M. Henmi, R. Yoshida, S. Eguchi (2007) Importance sampling with the estimated sampler, Biometrika, 94(4):985-991. [Biometrika]

P.K. Gupta*, R. Yoshida*, S. Imoto, R. Yamaguchi, S. Miyano (2007) 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. (ISBRA2007: Refereed conference) (* These authors equally contributed this work) [SpringerLink]

R. Yamaguchi, R. Yoshida, S. Imoto, T. Higuchi, S. Miyano (2007) Finding module-based gene networks in time-course gene expression data with state space models, IEEE Signal Processing Magazine, 24(1):37-46. [IEEE Xplore]

S. Tasaki, M. Nagasaki, M. Oyama, H. Hata, K. Ueno, R. Yoshida, T. Higuchi, S. Sugano, S. Miyano (2006) Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data, Genome Informatics, 17(2):226-238. [PubMed][JSBI]

R. Yoshida*, K. Numata*, S. Imoto, M. Nagasaki, A. Doi, K. Ueno, S. Miyano (2006) 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. (* These authors equally contributed this work) [PubMed][JSBI][Supplementary Information]

M. Nagasaki, R. Yamaguchi, R. Yoshida, S. Imoto, A. Doi, Y. Tamada, H. Matsuno, S. Miyano, T. Higuchi (2006) Genomic data assimilation for estimating Hybrid Functional Petri Net from time-course gene expression data, Genome Informatics, 17(1):46-61. [PubMed][JSBI]

R. Yoshida, T. Higuchi, S. Imoto, S. Miyano (2006) ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles, Bioinformatics, 22(12):1538-1539. [PubMed][Bioinformatics][Software]

R. Yoshida, T. Higuchi, S. Imoto (2005) 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. [PubMed][CSB2005]

R. Yoshida, S. Imoto, T. Higuchi (2005) 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. [SpringerLink]

R. Yoshida (2004) Method for approximating target distribution of Importance Sampling, Journal of the Statistical Society Japanese Issue, 34:21-37.

R. Yoshida, T. Higuchi, S. Imoto (2004) 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. [PubMed][CSB2004]