Hitoshi Koyano, Ph.D.

Table of Contents

1 About me

Senior Researcher

Multivariate Modeling Unit, AI Research Laboratory,

Research Center for Agricultural Information Technology,

National Agriculture and Food Research Organization

1-31-1 Kannondai, Tsukuba, Ibaraki 305-0856, Japan

Tel: +81-70-4484-7603

Email: koyanoh317@affrc.go.jp

Research interests: Mathematical modeling and analysis, scientific computing, mathematical informatics, data analysis

子猫と私

2 My publications

2.1 Journal papers

2.1.1 First author papers

  1. Hitoshi Koyano, Minimax empirical Bayes estimators in multivariate mixed linear models with unequal replications, Communications in Statistics: Theory and Methods, 35 (1), 121–131, 2006. https://www.tandfonline.com/doi/abs/10.1080/03610920500439430

  2. Hitoshi Koyano and Hirohisa Kishino, Observation subarea decision and population density estimation by space scale-invariance, Journal of the Japan Statistical Society, 39 (1), 77–88, 2009. https://www.jstage.jst.go.jp/article/jjss/39/1/39_1_77/_article/-char/ja/ <download>

  3. Hitoshi Koyano and Hirohisa Kishino, Quantifying biodiversity and asymptotics for a sequence of random strings, Physical Review E, 81 (6), 061912, 2010.
    https://journals.aps.org/pre/abstract/10.1103/PhysRevE.81.061912
    ∗ This paper was selected as an issue of June 15, 2010 of Virtual Journal of Biological Physics Research by the American Physical Society and the American Institute of Physics.

  4. Hitoshi Koyano and Kazuo Shigemasu, Trends in recent university entrance examinations in terms of achievement tests, an essay, and an interview, Journal of the Study of College Admissions, 22, 173–180, 2012. (in Japanese)
    <download>

  5. Hitoshi Koyano, Measuring α diversity and the theory of random strings, Proceedings of the Institute of Statistical Mathematics, 60 (2), 263–278, 2012. (in Japanese)
    https://www.ism.ac.jp/editsec/toukei/abstract/60-2j.html#263 <download>

  6. Hitoshi Koyano, Dimitar Serbezov, Hirohisa Kishino, and Tore Schweder, Fractional parentage analysis and a scale-free reproductive network of brown trout, Journal of Theoretical Biology, 336, 18–35, 2013. https://www.sciencedirect.com/science/article/abs/pii/S0022519313003214

  7. Hitoshi Koyano, Taishi Tsubouchi, Hirohisa Kishino, and Tatsuya Akutsu, Archaeal β diversity patterns under the seafloor along geochemical gradients, Journal of Geophysical Research G: Biogeosciences, 119 (9), 1770–1788, 2014. https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2014JG002676 <download>

  8. Hitoshi Koyano, Morihiro Hayashida, and Tatsuya Akutsu, Maximum margin classifier working in a set of strings, Proceedings of the Royal Society A, 472 (2187), 20150551, 2016. https://royalsocietypublishing.org/doi/full/10.1098/rspa.2015.0551 <download>

  9. Hitoshi Koyano, Morihiro Hayashida, and Tatsuya Akutsu, Optimal string clustering based on a Laplace-like mixture and EM algorithm on a set of strings, Journal of Computer and System Sciences, 106, 94–128, 2019. https://www.sciencedirect.com/science/article/abs/pii/S0022000019300509

  10. Hitoshi Koyano, Typology of patterns in cycles of the creation and discontinuation of foods: Toward modeling the life cycles of foods, Food Studies, 11 (2), 39–63, 2021.
    https://doi.org/10.18848/2160-1933/CGP/v11i02/39-63
    ∗ This paper was selected for the International Award for Excellence for Food Studies.

  11. Hitoshi Koyano and Erika Kamada, Development of a system for predicting and simulating the growth of spinach (Spinacia oleracea), Agricultural Information Research, 31 (1), 1–12, 2022. https://www.jstage.jst.go.jp/article/air/31/1/31_1/_article/-char/ja/ <download>

  12. Hitoshi Koyano, Kazunori Sawada, Nozomi Yamamoto, and Takuji Yamada, Modeling and analysis of the dynamics of communities of microbial DNA sequences in environments, Nonlinear Dynamics, 111 (6), 5767–5797, 2023. https://link.springer.com/article/10.1007/s11071-022-08105-y

  13. Hitoshi Koyano and Morihiro Hayashida, Volume formula and growth rates of the balls of strings under the edit distances, Applied Mathematics and Computation, 458, 128202, 2023.
    https://www.sciencedirect.com/science/article/abs/pii/S0096300323003715

  14. Hitoshi Koyano and Koji Yano, Evolutionary model of a population of DNA sequences through the interaction with an environment and its application to speciation analysis, submitted
    https://arxiv.org/abs/1706.01182.

  15. Hitoshi Koyano, Predicting progressive hearing loss based on auditory brainstem responses by using a novel dimension reduction method, submitted.

2.1.2 Collaboration papers

  1. Morihiro Hayashida and Hitoshi Koyano, Finding median and center strings for a probability distribution on a set of strings under Levenshtein distance based on integer linear programming, Communications in Computer and Information Science, 690, 108–121, 2017.
    https://link.springer.com/chapter/10.1007/978-3-319-54717-6_7

  2. Morihiro Hayashida, Mayumi Kamada, and Hitoshi Koyano, Improving conditional random field model for prediction of protein-RNA residue-base contacts, Quantitative Biology, 6 (2), 155–162, 2018. https://link.springer.com/article/10.1007/s40484-018-0136-7 <download>

  3. Tomoko Mihara, Hitoshi Koyano, Pascal Hingamp, Nigel Grimsley, Susumu Goto, and Hiroyuki Ogata, Taxon richness of ``Megaviridae'' exceeds those of Bacteria and Archaea in the ocean, Microbes and Environments, 33 (2), 162–171, 2018.
    https://pubmed.ncbi.nlm.nih.gov/29806626/ <download>

  4. Kazunori Sawada, Hitoshi Koyano, Nozomi Yamamoto, and Takuji Yamada, The relationships between microbiota and the amino acids and organic acids in commercial vegetable pickle fermented in rice-bran beds, Scientific Reports, 11 (1), 1791, 2021.
    https://www.nature.com/articles/s41598-021-81105-x <download>

  5. Kazunori Sawada, Hitoshi Koyano, Nozomi Yamamoto, and Takuji Yamada, The effects of vegetable pickling conditions on the dynamics of microbiota and metabolites, PeerJ, 9, e11123, 2021.
    https://peerj.com/articles/11123/ <download>

  6. Nozomi Yamamoto, Naoki Watarai, Hitoshi Koyano, Kazunori Sawada, Atsushi Toyoda, Ken Kurokawa, and Takuji Yamada, Analysis of genomic characteristics and their influence on metabolism in Aspergillus luchuensis albino mutants using genome sequencing, Fungal Genetics and Biology, 155, 103601, 2021. https://www.sciencedirect.com/science/article/pii/S1087184521000852 <download>

  7. Chisa Iwasaki, Hiroyoshi Sugiura, Genichiro Kikui, and Hitoshi Koyano, Nonlinear multivariate prediction model of `Kyoho' grape full bloom dates in Japan, Horticulture Journal, 91 (2), 195–208, 2022. https://www.jstage.jst.go.jp/article/hortj/advpub/0/advpub_UTD-349/_article/-char/en <download>

  8. Hideaki Oike, Satoru Tomita, Hitoshi Koyano, and Kayo Azami, Garland chrysanthemum consumption ameliorates age-related hearing loss in C57BL/6 mouse, Bioscience, Biotechnology, and Biochemistry, 86 (8), 1085–1094, 2022. https://academic.oup.com/bbb/article/86/8/1085/6605277

2.2 Conference papers

  1. Morihiro Hayashida, Hitoshi Koyano, and Tatsuya Akutsu, Measuring the similarity of protein structures using image local feature descriptors SIFT and SURF, 2014 8th International Conference on Systems Biology, 167–171, 2014.
    https://ieeexplore.ieee.org/abstract/document/6990750 <download>

  2. Morihiro Hayashida and Hitoshi Koyano, Integer linear programming approach to median and center strings for a probability distribution on a set of strings, Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, 3, 35–41, 2016.
    https://www.scitepress.org/PublicationsDetail.aspx?ID=1CNWp7pBGfE=&t=1 <download>

  3. Morihiro Hayashida, Mayumi Kamada, and Hitoshi Koyano, Predicting strengths of protein-protein interactions through online regression algorithms, Proceedings of the 2017 International Conference on Parallel and Distributed Processing Techniques and Applications, 261–264, 2017.

  4. Morihiro Hayashida, Hitoshi Koyano, and Tatsuya Akutsu, Grammar-based compression for directed and undirected generalized series-parallel graphs using integer linear programming, Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 4: Bioinformatics, 105–111, 2018.
    https://www.scitepress.org/PublicationsDetail.aspx?ID=NJ1MTfZLKVc=&t=1 <download>

  5. Morihiro Hayashida, Kousei Ishibashi, and Hitoshi Koyano, Analyzing order of domains in grammar-based compression of a proteome, Proceedings of the 2018 International Conference on Parallel and Distributed Processing Techniques and Applications, 278–281, 2018.

  6. Morihiro Hayashida, Jose Nacher, and Hitoshi Koyano, Artificial neural network approach to prediction of protein–RNA residue-base contacts, Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 3: Bioinformatics, 163–167, 2019.
    https://www.scitepress.org/PublicationsDetail.aspx?ID=18gs+ld+ePs=&t=1 <download>

  7. Morihiro Hayashida, Hitoshi Koyano, and Jose Nacher, Measuring the similarity of proteomes using grammar-based compression via domain combinations, Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 3: Bioinformatics, 117–122, 2020.
    https://www.scitepress.org/PublicationsDetail.aspx?ID=KfGh2HeJHCc=&t=1 <download>

  8. Kazunori Sawada, Nozomi Yamamoto, Hitoshi Koyano, Hinako Yago, and Takuji Yamada, The factors of modifying indigenous preserved foods, In Eric Olmendo and Rachel Chan Suet Kay (Eds.) Indigeneity and Food: Politics, Transnationalism and Social Inclusion, Penerbit Universiti Kebangsaan Malaysia, 66–80, 2021.

3 My softwares

  1. stringsphere
    stringsphere is a softwawe implemented in C++ for computing estimates of the volumes of balls and spheres of strings in the metric space of all strings composed of a given alphabet provided with several types of edit distance.
    Please see the following paper for how and why this softwawe was developed.

    • Hitoshi Koyano and Morihiro Hayashida, Volume formula and growth rates of the balls of strings under the edit distances, submitted.

    In this paper, the volume formula for the balls of strings under the extended Hamming distance was derived. Furthermore, a randomized algorithm was formulated for computing estimates of the volumes of balls of strings efficiently, and by performing numerical experiments using the algorithm, a conjecture was presented on the volume formula for the balls of strings under the Lavenshtein distance.
    <download>

  2. SukuSuku Horensou
    SukuSuku Horensou is a software implemented in C++ for predicting and simulating the growth of spinach (Spinacia oleracea) to assist farmers in planning harvests and shipments. SukuSuku Hourensou can adjust prediction and simulation results by using vegetation cover rates obtained from airborne images taken by drones. Furthermore, to support planning of harvests and shipments, SukuSuku Hourensou can suggest harvest times that smooth shipment fresh weights over a harvest season.
    Please see the following papers for details of the growth model and computing algorithms implemented in SukuSuku Hourensou.

    • Erika Kamada, Takanori Ishii, and Kunihiko Okada, Effect of temperature and solar radiation on dry matter production of spinach for processing use, Horticulture Research, 20 (4), 423–432, 2021. (in Japanese)
    • Hitoshi Koyano and Erika Kamada, Development of a system for predicting and simulating the growth of spinach (Spinacia oleracea), Agricultural Information Research, 31 (1), 1–12, 2022.

    SukuSuku Hourensou is available by going through the procedure to use the work made for hire of registration number ZC1 at the intellectual property department of the National Agriculture and Food Research Organization.

  3. Persimmon Forcaster
    Persimmon Forcaster is a software implemented in R for estimating the parameters of the models of persimmon blossoming, fruit enlargement, and pigmentation and predicting the growth of persimmon.
    Persimmon Forcaster is available by going through the procedure to use the work made for hire of registration number ZC11 at the intellectual property department of the National Agriculture and Food Research Organization.

  4. Extended Hearing Predictor
    Extended Hearing Predictor is a software implemented in R for predicting future variations in hearing based on auditory brainstem responses by using my developed dimension reduction method.
    <download>

Author: Hitoshi Koyano

Created: 2023-09-09 土 15:45

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