Hidetoshi Matsui

554 total citations
33 papers, 372 citations indexed

About

Hidetoshi Matsui is a scholar working on Statistics and Probability, Molecular Biology and Cancer Research. According to data from OpenAlex, Hidetoshi Matsui has authored 33 papers receiving a total of 372 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Statistics and Probability, 11 papers in Molecular Biology and 5 papers in Cancer Research. Recurrent topics in Hidetoshi Matsui's work include Statistical Methods and Inference (12 papers), Advanced Statistical Methods and Models (7 papers) and Control Systems and Identification (4 papers). Hidetoshi Matsui is often cited by papers focused on Statistical Methods and Inference (12 papers), Advanced Statistical Methods and Models (7 papers) and Control Systems and Identification (4 papers). Hidetoshi Matsui collaborates with scholars based in Japan, United Kingdom and Poland. Hidetoshi Matsui's co-authors include Sadanori Konishi, Shuichi Kawano, Yuko Araki, Koichi Kawamoto, Naohiro Nishida, Jun Koseki, Masamitsu Konno, Masaki Mori, Yuichiro� Doki and Hideshi Ishii and has published in prestigious journals such as PLoS ONE, Scientific Reports and Frontiers in Plant Science.

In The Last Decade

Hidetoshi Matsui

32 papers receiving 367 citations

Peers

Hidetoshi Matsui

José R. Berrendero Spain

Susmita Datta United States

Grace S. Shieh Taiwan

Beatrix Jones New Zealand

Yuan‐chin Ivan Chang Taiwan

Weichen Wang United States

Changyi Park South Korea

Ming Ouyang United States

Shunfang Wang China

Melanie Osl Austria

José R. Berrendero Spain

Hidetoshi Matsui

178 ×1.2

163 ×1.5

110 ×1.1

91 ×1.9

29 ×1.0

Citations per year, relative to Hidetoshi Matsui Hidetoshi Matsui (= 1×) peers José R. Berrendero

Countries citing papers authored by Hidetoshi Matsui

Since Specialization

Citations

This map shows the geographic impact of Hidetoshi Matsui's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hidetoshi Matsui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hidetoshi Matsui more than expected).

Fields of papers citing papers by Hidetoshi Matsui

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hidetoshi Matsui. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hidetoshi Matsui. The network helps show where Hidetoshi Matsui may publish in the future.

Co-authorship network of co-authors of Hidetoshi Matsui

This figure shows the co-authorship network connecting the top 25 collaborators of Hidetoshi Matsui. A scholar is included among the top collaborators of Hidetoshi Matsui based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Hidetoshi Matsui. Hidetoshi Matsui is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Ise, Takeshi, et al.. (2024). Social factors of urban greening: Demographics, zoning, and social capital. City and Environment Interactions. 24. 100160–100160. 1 indexed citations

Matsui, Hidetoshi, et al.. (2023). Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability. Neural Computation. 35(5). 977–994. 1 indexed citations

Fukuda, Tatsuya, Hidetoshi Matsui, Hiroya Takada, Toshihiro Misumi, & Sadanori Konishi. (2023). Multivariate functional subspace classification for high-dimensional longitudinal data. Japanese Journal of Statistics and Data Science. 7(1). 1–16. 1 indexed citations

Konno, Masamitsu, Hidetoshi Matsui, Jun Koseki, et al.. (2018). Computational trans-omics approach characterised methylomic and transcriptomic involvements and identified novel therapeutic targets for chemoresistance in gastrointestinal cancer stem cells. Scientific Reports. 8(1). 899–899. 4 indexed citations

Smaga, Łukasz & Hidetoshi Matsui. (2018). A note on variable selection in functional regression via random subspace method. Statistical Methods & Applications. 27(3). 455–477. 6 indexed citations

Kawamoto, Koichi, Masamitsu Konno, Naohiro Nishida, et al.. (2018). Cell‑free culture conditioned medium elicits pancreatic β cell lineage‑specific epigenetic reprogramming in mice. Oncology Letters. 16(3). 3255–3259. 3 indexed citations

Matsui, Hidetoshi. (2018). Sparse group lasso for multiclass functional logistic regression models. Communications in Statistics - Simulation and Computation. 48(6). 1784–1797. 11 indexed citations

Koseki, Jun, Hidetoshi Matsui, Masamitsu Konno, et al.. (2016). A Trans-omics Mathematical Analysis Reveals Novel Functions of the Ornithine Metabolic Pathway in Cancer Stem Cells. Scientific Reports. 6(1). 20726–20726. 11 indexed citations

Miyo, Masaaki, Masamitsu Konno, Naohiro Nishida, et al.. (2016). Metabolic Adaptation to Nutritional Stress in Human Colorectal Cancer. Scientific Reports. 6(1). 38415–38415. 55 indexed citations

10.

Konno, Masamitsu, Jun Koseki, Nobuhiro Tanuma, et al.. (2015). Pyruvate kinase M2, but not M1, allele maintains immature metabolic states of murine embryonic stem cells. Regenerative Therapy. 1. 63–71. 6 indexed citations

11.

Kayano, Mitsunori, Hidetoshi Matsui, Rui Yamaguchi, Seiya Imoto, & Satoru Miyano. (2015). Gene set differential analysis of time course expression profiles via sparse estimation in functional logistic model with application to time-dependent biomarker detection. Biostatistics. 17(2). 235–248. 12 indexed citations

12.

Konno, Masamitsu, Jun Koseki, Koichi Kawamoto, et al.. (2015). Embryonic MicroRNA-369 Controls Metabolic Splicing Factors and Urges Cellular Reprograming. PLoS ONE. 10(7). e0132789–e0132789. 16 indexed citations

13.

Koseki, Jun, Hugh Shunsuke Colvin, Takahito Fukusumi, et al.. (2015). Mathematical analysis predicts imbalanced IDH1/2 expression associates with 2-HG-inactivating β-oxygenation pathway in colorectal cancer. International Journal of Oncology. 46(3). 1181–1191. 13 indexed citations

14.

Matsui, Hidetoshi. (2015). SPARSE REGULARIZATION FOR BI-LEVEL VARIABLE SELECTION. 28(1). 83–103. 2 indexed citations

15.

Ogawa, Hisataka, Xin Wu, Koichi Kawamoto, et al.. (2015). MicroRNAs Induce Epigenetic Reprogramming and Suppress Malignant Phenotypes of Human Colon Cancer Cells. PLoS ONE. 10(5). e0127119–e0127119. 30 indexed citations

16.

Matsui, Hidetoshi & Sadanori Konishi. (2011). Variable selection for functional regression models via the regularization. Computational Statistics & Data Analysis. 55(12). 3304–3310. 59 indexed citations

17.

Matsui, Hidetoshi, et al.. (2009). Effects of hypoxia on behavior and mortality of pen shell (Atrina lischkeana). 64(1). 19–22. 3 indexed citations

18.

Kobayashi, Mai & Hidetoshi Matsui. (2006). Surface chemical composition of soil aggregates as a controlling factor of water repellency of forest soils. 1 indexed citations

19.

Fukui, Shigehisa, Hidetoshi Matsui, Kiyomi YAMANE, & Hiroshige MATSUOKA. (2005). Dynamic characteristics of flying head slider with ultra thin spacing (CIP method and linearized method). Microsystem Technologies. 11(8-10). 812–818. 2 indexed citations

20.

Matsui, Hidetoshi, et al.. (2003). Surface chemical analysis of water repellent forest soil. EAEJA. 13491. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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