Publications

[52] Kato, A., Iwasaki, R., Takeshima, K., Maruzuru, Y., Koyanagi, N., Natsume, T., Kusano, H., Adachi, S., Kawano, S. and Kawaguchi, Y. (2024) Identification of a novel neurovirulence factor encoded by the cryptic orphan gene UL31.6 of herpes simplex virus 1. Journal of Virology (in press) (doi: 10.1128/jvi.00747-24).

[51] Kakikawa, Y. and Kawano, S. (2024) Bayesian fused lasso modeling for binary data. Behaviormetrika (online first)  (doi: 10.1007/s41237-024-00231-8) [fulltext].

[50] Okazaki, A. and Kawano, S. (2024) Multi-task learning regression via convex clustering. Computational Statistics & Data Analysis, 195, 107956 (doi: 10.1016/j.csda.2024.107956).

[49] Kakikawa, Y., Shimamura, K. and Kawano, S. (2023) Bayesian fused lasso modeling via horseshoe prior. Japanese Journal of Statistics and Data Science, 6, 705-727 (doi: 10.1007/s42081-023-00213-2). 

[48] Murayama, K. and Kawano, S. (2023) Sparse Bayesian learning with weakly informative hyperprior and extended predictive information criterion. IEEE Transactions on Neural Networks and Learning Systems, 34, 5856-5868 (doi: 10.1109/TNNLS.2021.3131357).

[47] Yoshikawa, K. and Kawano, S. (2023) Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization. Computational Statistics, 38, 53-75 (doi: 10.1007/s00180-022-01216-5). (Online Supplement) (GitHub “RVSManOpt”)

[46] Kim, D., Kawano, S. and Ninomiya, Y. (2023) Smoothly varying regularization. Computational Statistics & Data Analysis, 179, 107644 (doi: 10.1016/j.csda.2022.107644). (GitHub “SVaRu”)

[45] Okazaki, A. and Kawano, S. (2022) Multi-task learning for compositional data via sparse network lasso. Entropy, 24, 1839 (doi: 10.3390/e24121839).  (GitHub “CSNL”)

[44] Oshiki, M., Fukushima, T., Kawano, S. and Nakagawa, J. (2022) Endpoint recombinase polymerase amplification (RPA) assay for enumeration of thiocyanate-degrading bacteria. Microbes and Environments, 37, ME21073 (doi: 10.1264/jsme2.ME21073). 

[43] Takahashi, H., Tahara, M., Miyashita, S., Ando, S., Kawano, S., Kanayama, Y., Fukuhara, T. and Kormelink, R. (2022) Cucumber Mosaic Virus Infection in Arabidopsis: A Conditional Mutualistic Symbiont? Frontiers in Microbiology, 12, 770925 (doi: 10.3389/fmicb.2021.770925).

[42] Shimamura, K. and Kawano, S. (2021) Bayesian sparse convex clustering via global-local shrinkage priors. Computational Statistics, 36, 2671-2699 (doi: 10.1007/s00180-021-01101-7).

[41] Yoshikawa, K. and Kawano, S. (2021) Multilinear common component analysis via Kronecker product representation. Neural Computation, 33, 2853-2880 (doi: 10.1162/neco_a_01425). (GitHub “MCCA”)

[40] Kawano, S. (2021) Sparse principal component regression via singular value decomposition approach. Advances in Data Analysis and Classification, 15, 795-823 (doi: 10.1007/s11634-020-00435-2). (Online Supplement) (GitHub “spcr-svd”)

[39] Wu, S., Shimamura, K., Yoshikawa, K., Murayama, K. and Kawano, S. (2021) Variable fusion for Bayesian linear regression via spike-and-slab priors. Proc. the 13th KES International Conference on Intelligent Decision Technologies (KES-IDT-21), 238, 491-501 (doi: 10.1007/978-981-16-2765-1_41).

[38] Yoshida, H., Kawano, S. and Ninomiya, Y. (2021) Discriminant analysis via smoothly varying regularization. Proc. the 13th KES International Conference on Intelligent Decision Technologies (KES-IDT-21), 238, 441-455 (doi: 10.1007/978-981-16-2765-1_37).

[37] Miyazaki, K., Suenaga, H., Oshiki, M., Kawano, S. and Fukushima, T. (2021) Complete genome sequence of Thiohalobacter sp. strain COW1, isolated from activated sludge treating coke oven wastewater. Microbiology Resource Announcements, 10, e00013-21 (doi: 10.1128/MRA.00013-21).

[36] Kojima, S., Yoshikawa, K., Ito, J., Nakagawa, S., Parrish, N.F., Horie, M., Kawano, S. and Tomonaga, K. (2021) Virus-like insertions with sequence signatures similar to those of endogenous non-retroviral RNA viruses in the human genome. Proceedings of the National Academy of Sciences of the United States of America, 118, e2010758118 (doi: 10.1073/pnas.2010758118). (URL)

[35] Ohishi, M., Yanagihara, H. and Kawano, S. (2020) Equivalence between adaptive-lasso and generalized ridge estimators in linear regression with orthogonal explanatory variables after optimizing regularization parameters. Annals of the Institute of Statistical Mathematics, 72, 1501-1516 (doi: 10.1007/s10463-019-00734-2).

[34] Kato, A., Adachi, S., Kawano, S., Takeshima, K., Watanabe, M., Kitazume, S., Sato, R., Kusano, H., Koyanagi, N., Maruzuru, Y., Arii, J., Hatta, T., Natsume, T. and Kawaguchi, Y. (2020) Identification of a herpes simplex virus 1 gene encoding neurovirulence factor by chemical proteomics. Nature Communications, 11, 4894 (doi: 10.1038/s41467-020-18718-9). (press release)

[33] Oshiki, M., Fukushima, T., Kawano, S., Kasahara, Y. and Nakagawa, J. (2019) Thiocyanate degradation by a highly enriched culture of the neutrophilic halophile Thiohalobacter sp. strain FOKN1 from activated sludge and genomic insights into thiocyanate metabolism. Microbes and Environments, 34, 402-412 (doi: 10.1264/jsme2.ME19068).

[32] Shimamura, K., Ueki, M., Kawano, S. and Konishi, S. (2019) Bayesian generalized fused lasso modeling via NEG distribution. Communications in Statistics - Theory and Methods, 48, 4132-4153 (doi: 10.1080/03610926.2018.1489056). (R package “neggfl”)

[31] Matsuda, K., Kawano, S. and Konishi, S. (2018) Predictive information criteria for robust relevance vector regression models. Bulletin of Informatics and Cybernetics, 50, 67-80 (doi: 10.5109/2233862).

[30] Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2018) Sparse principal component regression for generalized linear models. Computational Statistics & Data Analysis, 124, 180-196 (doi: 10.1016/j.csda.2018.03.008). (Online Supplement) (R package “spcr”)

[29] Yamasaki, Y., Fujita, M., Kawano, S. and Baba, T. (2018) Effect of salinity on interspecific competition between the dinoflagellate Alexandrium catenella and the raphidophyte Heterosigma akashiwo. Aquatic Microbial Ecology, 81, 73-82 (doi: 10.3354/ame01860).

[28] Oshiki, M., Fukushima, T., Kawano, S. and Nakagawa, J. (2017) Draft genome sequence of Thiohalobacter thiocyanaticus strain FOKN1, a neutrophilic halophile capable of thiocyanate degradation. Genome Announcements, 5, e00799-17 (doi: 10.1128/genomeA.00799-17).

[27] Ninomiya, Y. and Kawano, S. (2016) AIC for the Lasso in generalized linear models. Electronic Journal of Statistics, 10, 2537-2560 (doi: 10.1214/16-EJS1179). (R package “sAIC”)

[26] Yamasaki, Y., Taga, S., Kishioka, M. and Kawano, S. (2016) A metabolic profile in Ruditapes philippinarum associated with growth-promoting effects of alginate hydrolysates. Scientific Reports, 6, 29923 (doi: 10.1038/srep29923).

[25] Natori, K., Uto, M., Nishiyama, Y., Kawano, S. and Ueno, M. (2015) Constraint-based learning Bayesian networks using Bayes factor. Proc. 2nd International Workshop, Advanced Methodologies for Bayesian Networks 2015, Lecture Notes in Artificial Intelligence, 9505, 15-31, Springer (doi: 10.1007/978-3-319-28379-1_2).

[24] Kawano, S., Hoshina, I., Shimamura, K. and Konishi, S. (2015) Predictive model selection criteria for Bayesian lasso regression. Journal of the Japanese Society of Computational Statistics, 28, 67-82 (doi: 10.5183/jjscs.1501001_220).

[23] Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2015) Sparse principal component regression with adaptive loading. Computational Statistics & Data Analysis, 89, 192-203 (doi: 10.1016/j.csda.2015.03.016 ). (Online Supplement) (R package “spcr”)

[22] Kawano, S. (2014) Selection of tuning parameters in bridge regression models via Bayesian information criterion. Statistical Papers, 55, 1207-1223 (doi: 10.1007/s00362-013-0561-7). (Errata)

[21] Kim, D., Kawano, S. and Ninomiya, Y. (2014) Adaptive basis expansion via $\ell_1$ trend filtering. Computational Statistics, 29, 1005-1023 (doi: 10.1007/s00180-013-0477-7).

[20] Matsui, H., Misumi, T. and Kawano, S. (2014) Model selection criteria for the varying-coefficient modeling via regularized basis expansions. Journal of Statistical Computation and Simulation, 84, 2156-2165 (doi: 10.1080/00949655.2013.785548).

[19] Kawano, S. (2013) Semi-supervised logistic discrimination via labeled data and unlabeled data from different sampling distributions. Statistical Analysis and Data Mining, 6, 472-481 (doi: 10.1002/sam.11204).

[18] Takatsuno, Y., Mimori, K., Yamamoto, K., Sato, T., Niida, A., Inoue, H., Imoto, S., Kawano, S., Yamaguchi, R., Toh, H., Iinuma, H., Ishimaru, S., Ishii, H., Suzuki, S., Tokudome, S., Watanabe, M., Tanaka, J. I., Kudo, S. E., Mochizuki, H., Kusunoki, M., Yamada, K., Shimada, Y., Moriya, Y., Miyano, S., Sugihara, K. and Mori, M. (2013) The rs6983267 SNP is associated with MYC transcription efficiency, which promotes progression and worsens prognosis of colorectal cancer. Annals of Surgical Oncology, 20, 1395-1402 (doi: 10.1245/s10434-012-2657-z).

[17] Kawano, S. (2012) Adaptive bridge regression modeling and selection of the tuning parameters. Bulletin of Informatics and Cybernetics, 44, 29-39 (doi: 10.5109/1495409).

[16] Kim, D., Kawano, S. and Konishi, S. (2012) Predictive information criteria for Bayesian nonlinear regression models. Bulletin of Informatics and Cybernetics, 44, 17-28 (doi: 10.5109/1495408).

[15] Kawano, S. and Konishi, S. (2012) Semi-supervised logistic discrimination for functional data. Bulletin of Informatics and Cybernetics, 44, 1-15 (doi: 10.5109/1495407).

[14] Kawano, S., Misumi, T. and Konishi, S. (2012) Semi-supervised logistic discrimination via graph-based regularization. Neural Processing Letters, 36, 203-216 (doi: 10.1007/s11063-012-9231-3).

[13] Ishimaru, S., Mimori, K., Yamamoto, K., Inoue, H., Imoto, S., Kawano, S., Yamaguchi, R., Sato, T., Toh, H., Iinuma, H., Maeda, T., Ishii, H., Suzuki, S., Tokudome, S., Watanabe, M., Tanaka, J., Kudo, S., Sugihara, K., Hase, K., Mochizuki, H., Kusunoki, M., Yamada, K., Shimada, Y., Moriya, Y., Barnard, G. F., Miyano, S. and Mori, M. (2012) Increased risk for CRC in diabetic patients with the nonrisk allele of SNPs at 8q24. Annals of Surgical Oncology, 19, 2853-2858 (doi: 10.1245/s10434-012-2278-6).

[12] Kawano, S., Shimamura, T., Niida, A., Imoto, S., Yamaguchi, R., Nagasaki, M., Yoshida, R., Print, C. and Miyano, S. (2012) Identifying gene pathways associated with cancer characteristics via sparse statistical methods. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9, 966-972 (doi: 10.1109/TCBB.2012.48). (Online Supplement)

[11] Okayama, H., Kohno, T., Ishii, Y., Shimada, Y., Shiraishi, K., Iwakawa, R., Furuta, K., Tsuta, K., Shibata, T., Yamamoto, S., Watanabe, S., Sakamoto, H., Kumamoto, K., Takenoshita, S., Gotoh, N., Kawano, S., Yamaguchi, R., Miyano, S. and Yokota, J. (2012) Identification of genes up-regulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Research, 72, 100-111 (doi: 10.1158/0008-5472.CAN-11-1403).

[10] Kawano, S. and Konishi, S. (2011) Semi-supervised logistic discrimination via regularized Gaussian basis expansions. Communications in Statistics - Theory and Methods, 40, 2412-2423 (doi: 10.1080/03610926.2010.481370).

[9] Hirose, K., Kawano, S., Konishi, S. and Ichikawa, M. (2011) Bayesian information criterion and selection of the number of factors in factor analysis models. Journal of Data Science, 9, 243-259 (doi: 10.6339/JDS.201104_09(2).0007).

[8] Hirose, K., Kawano, S., Miike, D. and Konishi, S. (2010) Hyper-parameter selection in Bayesian structural equation models. Bulletin of Informatics and Cybernetics, 42, 55-70 (doi: 10.5109/25906).

[7] Kawano, S., Shimamura, T., Niida, A., Imoto, S., Yamaguchi, R., Nagasaki, M., Yoshida, R., Print, C. and Miyano, S. (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%)) (doi: 10.1109/BIBM.2010.5706572)

[6] Araki, Y., Konishi, S., Kawano, S. and Matsui, H. (2009) Functional regression modeling via regularized Gaussian basis expansions. Annals of the Institute of Statistical Mathematics, 61, 811-833 (doi: 10.1007/s10463-007-0161-1).

[5] Araki, Y., Konishi, S., Kawano, S. and Matsui, H. (2009) Functional logistic discrimination via regularized basis expansions. Communications in Statistics - Theory and Methods, 38, 2944-2957 (doi: 10.1080/03610920902947246).

[4] Kawano, S. and Konishi, S. (2009) Nonlinear logistic discrimination via regularized Gaussian basis expansions. Communications in Statistics - Simulation and Computation, 38, 1414-1425 (doi: 10.1080/03610910902940150).

[3] Matsui, H., Kawano, S. and Konishi, S. (2009) Regularized functional regression modeling for functional response and predictors. Journal of Mathematics for Industry, 1, 17-25 [PDF].

[2] Hirose, K., Kawano, S. and Konishi, S. (2008) Bayesian factor analysis and information criterion. Bulletin of Informatics and Cybernetics, 40, 75-87 (doi: 10.5109/18995).

[1] Kawano, S. and Konishi, S. (2007) Nonlinear regression modeling via regularized Gaussian basis functions. Bulletin of Informatics and Cybernetics, 39, 83-96 (doi: 10.5109/16776).