Masashi Honma

1.8k total citations · 1 hit paper
51 papers, 1.2k citations indexed

About

Masashi Honma is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Masashi Honma has authored 51 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 19 papers in Oncology and 7 papers in Computational Theory and Mathematics. Recurrent topics in Masashi Honma's work include Bone health and treatments (12 papers), Bone Metabolism and Diseases (12 papers) and Computational Drug Discovery Methods (7 papers). Masashi Honma is often cited by papers focused on Bone health and treatments (12 papers), Bone Metabolism and Diseases (12 papers) and Computational Drug Discovery Methods (7 papers). Masashi Honma collaborates with scholars based in Japan, United States and Austria. Masashi Honma's co-authors include Hiroshi Suzuki, Yuki Ikebuchi, Yoshiaki Kariya, Shigeki Aoki, Madoka Hayashi, Nobuyuki Udagawa, Kazuhiro Aoki, Yasutaka Sugamori, Yasuhiko Tabata and Masud Khan and has published in prestigious journals such as Nature, Biochemical and Biophysical Research Communications and Kidney International.

In The Last Decade

Masashi Honma

51 papers receiving 1.2k citations

Hit Papers

Coupling of bone resorption and formation by RANKL revers... 2018 2026 2020 2023 2018 100 200 300 400

Peers

Masashi Honma
Masashi Honma
Citations per year, relative to Masashi Honma Masashi Honma (= 1×) peers Arun Kumar Trivedi

Countries citing papers authored by Masashi Honma

Since Specialization
Citations

This map shows the geographic impact of Masashi Honma'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 Masashi Honma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masashi Honma more than expected).

Fields of papers citing papers by Masashi Honma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Masashi Honma. 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 Masashi Honma. The network helps show where Masashi Honma may publish in the future.

Co-authorship network of co-authors of Masashi Honma

This figure shows the co-authorship network connecting the top 25 collaborators of Masashi Honma. A scholar is included among the top collaborators of Masashi Honma 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 Masashi Honma. Masashi Honma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kariya, Yoshiaki & Masashi Honma. (2024). Applications of model simulation in pharmacological fields and the problems of theoretical reliability. Drug Metabolism and Pharmacokinetics. 56. 100996–100996. 3 indexed citations
2.
Honma, Masashi. (2023). The potential of RANKL reverse signaling as a novel pharmacological target. Folia Pharmacologica Japonica. 158(3). 253–257. 1 indexed citations
3.
Kariya, Yoshiaki, Masashi Honma, Keita Tokuda, Akihiko Konagaya, & Hiroshi Suzuki. (2022). Utility of constraints reflecting system stability on analyses for biological models. PLoS Computational Biology. 18(9). e1010441–e1010441. 1 indexed citations
4.
Honma, Masashi & Hiroshi Suzuki. (2022). Can molecular dynamics facilitate the design of protein–protein-interaction inhibitors?. Nature Reviews Rheumatology. 19(1). 8–9. 4 indexed citations
5.
Honma, Masashi, et al.. (2021). Study on Horizon Scanning by Citation Network Analysis and Text Mining: A Focus on Drug Development Related to T Cell Immune Response. Therapeutic Innovation & Regulatory Science. 56(2). 230–243. 4 indexed citations
6.
Sasaki, Hajime, et al.. (2021). Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis. Therapeutic Innovation & Regulatory Science. 56(2). 263–275. 6 indexed citations
7.
Honma, Masashi, Yuki Ikebuchi, & Hiroshi Suzuki. (2020). Mechanisms of RANKL delivery to the osteoclast precursor cell surface. Journal of Bone and Mineral Metabolism. 39(1). 27–33. 17 indexed citations
8.
Honma, Masashi, Yuki Ikebuchi, & Hiroshi Suzuki. (2020). RANKL as a key figure in bridging between the bone and immune system: Its physiological functions and potential as a pharmacological target. Pharmacology & Therapeutics. 218. 107682–107682. 34 indexed citations
9.
Noshiro, Daisuke, Yuki Ikebuchi, Masud Khan, et al.. (2018). The induction of RANKL molecule clustering could stimulate early osteoblast differentiation. Biochemical and Biophysical Research Communications. 509(2). 435–440. 18 indexed citations
10.
Kariya, Yoshiaki, Masashi Honma, & Hiroshi Suzuki. (2016). Mechanism analyses and mechanism-based prediction for adverse drug reactions using systems pharmacology. Folia Pharmacologica Japonica. 147(2). 89–94. 1 indexed citations
11.
Ito, Naoki, Kousei Ito, Yuki Ikebuchi, et al.. (2014). Organic Cation Transporter/Solute Carrier Family 22a is Involved in Drug Transfer into Milk in Mice. Journal of Pharmaceutical Sciences. 103(10). 3342–3348. 24 indexed citations
12.
Ito, Naoki, Kousei Ito, Akihiro Hisaka, et al.. (2013). Contribution of Protein Binding, Lipid Partitioning, and Asymmetrical Transport to Drug Transfer into Milk in Mouse Versus Human. Pharmaceutical Research. 30(9). 2410–2422. 16 indexed citations
13.
Ito, Kousei, et al.. (2011). Analysis and Prediction of Drug Transfer into Human Milk Taking into Consideration Secretion and Reuptake Clearances across the Mammary Epithelia. Drug Metabolism and Disposition. 39(12). 2370–2380. 30 indexed citations
14.
Yamamoto, Naoko, Masashi Honma, & Hiroshi Suzuki. (2011). Off-Target Serine/Threonine Kinase 10 Inhibition by Erlotinib Enhances Lymphocytic Activity Leading to Severe Skin Disorders. Molecular Pharmacology. 80(3). 466–475. 37 indexed citations
15.
Honma, Masashi, et al.. (2010). Methods for the quantitative evaluation and prediction of CYP enzyme induction using human in vitro systems. Expert Opinion on Drug Discovery. 5(5). 491–511. 6 indexed citations
16.
Ito, Kousei, et al.. (2010). Characterization of Inhibitory Effect of Carbapenem Antibiotics on the Deconjugation of Valproic Acid Glucuronide. Drug Metabolism and Disposition. 38(10). 1828–1835. 10 indexed citations
17.
Honma, Masashi, et al.. (2009). Quantitative Prediction of in Vivo Profiles of CYP3A4 Induction in Humans from in Vitro Results with a Reporter Gene Assay. Drug Metabolism and Disposition. 37(6). 1234–1241. 18 indexed citations
18.
Ito, Kousei, Masashi Honma, Yuki Ikebuchi, et al.. (2008). Posttranslational regulation of Abcc2 expression by SUMOylation system. American Journal of Physiology-Gastrointestinal and Liver Physiology. 296(2). G406–G413. 19 indexed citations
19.
Honma, Masashi, Masao Toyoda, Tomoya Umezono, et al.. (2008). An investigation of 2,093 renal biopsies performed at Tokai University Hospital between 1976 and 2000. Clinical Nephrology. 69(1). 18–23. 8 indexed citations
20.
Hoshi, Hideo, Katsuya Yamauchi, Kiyohisa Sekizawa, et al.. (1996). Nitrogen Dioxide Exposure Increases Airway Contractile Response to Histamine by Decreasing Histamine N-Methyltransferase Activity in Guinea Pigs. American Journal of Respiratory Cell and Molecular Biology. 14(1). 76–83. 5 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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