Hiroshi Haeno

1.7k total citations
29 papers, 1.0k citations indexed

About

Hiroshi Haeno is a scholar working on Cancer Research, Oncology and Modeling and Simulation. According to data from OpenAlex, Hiroshi Haeno has authored 29 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cancer Research, 14 papers in Oncology and 10 papers in Modeling and Simulation. Recurrent topics in Hiroshi Haeno's work include Cancer Genomics and Diagnostics (14 papers), Mathematical Biology Tumor Growth (10 papers) and Pancreatic and Hepatic Oncology Research (7 papers). Hiroshi Haeno is often cited by papers focused on Cancer Genomics and Diagnostics (14 papers), Mathematical Biology Tumor Growth (10 papers) and Pancreatic and Hepatic Oncology Research (7 papers). Hiroshi Haeno collaborates with scholars based in Japan, United States and United Kingdom. Hiroshi Haeno's co-authors include Franziska Michor, Mithat Gönen, Joseph M. Herman, Christine A. Iacobuzio–Donahue, Meghan B. Davis, Ross L. Levine, Yoh Iwasa, Inge Van de Walle, Kimiyo N. Yamamoto and Jiří Zavadil and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Hiroshi Haeno

28 papers receiving 1.0k citations

Peers

Hiroshi Haeno

ONCOL

PRM

Sara Widaa United Kingdom

Henry Lee-Six United Kingdom

Ahmet Acar Türkiye

Kerstin Haase Germany

Ronald N. Buick Canada

Susanna L. Cooke United Kingdom

Ruping Sun United States

Jennifer C. Finley United States

Rosa‐Ana Risques Spain

Bernhard Reis Switzerland

Sara Widaa United Kingdom

Hiroshi Haeno

354 ×0.8

ONCOL

762 ×1.7

632 ×2.0

81 ×0.5

PRM

32 ×0.2

Citations per year, relative to Hiroshi Haeno Hiroshi Haeno (= 1×) peers Sara Widaa

Countries citing papers authored by Hiroshi Haeno

Since Specialization

Citations

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

Fields of papers citing papers by Hiroshi Haeno

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hiroshi Haeno

This figure shows the co-authorship network connecting the top 25 collaborators of Hiroshi Haeno. A scholar is included among the top collaborators of Hiroshi Haeno 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 Hiroshi Haeno. Hiroshi Haeno 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

Saeki, Koichi, Masaki Nakamura, Hidehiro Hojo, et al.. (2024). Mathematical Modeling Predicts Optimal Immune Checkpoint Inhibitor and Radiotherapy Combinations and Timing of Administration. Cancer Immunology Research. 13(3). 353–364. 4 indexed citations

Sato, Taku, Hajime Sato, Fuyuki Miya, et al.. (2024). Comparative analysis of tongue cancer organoids among patients identifies the heritable nature of minimal residual disease. Developmental Cell. 60(3). 396–413.e6. 5 indexed citations

Watanabe, Masahiko, Hiroshi Haeno, Sachiyo Mimaki, & Katsuya Tsuchihara. (2024). Multistage carcinogenesis in occupational cholangiocarcinoma: the impact of clonal expansion and risk estimation. Genes and Environment. 46(1). 21–21.

Abubakar, Sharafudeen Dahiru, et al.. (2023). Computational modeling of locoregional recurrence with spatial structure identifies tissue-specific carcinogenic profiles. Frontiers in Oncology. 13. 1116210–1116210. 2 indexed citations

Iwai, Kenichi, Tadahiro Nambu, Yukie Kashima, et al.. (2021). A CDC7 inhibitor sensitizes DNA-damaging chemotherapies by suppressing homologous recombination repair to delay DNA damage recovery. Science Advances. 7(21). 19 indexed citations

Haeno, Hiroshi, et al.. (2021). Mathematical Modeling of Locoregional Recurrence Caused by Premalignant Lesions Formed Before Initial Treatment. Frontiers in Oncology. 11. 743328–743328. 2 indexed citations

Morita, Tomoko, Akihiro Ohashi, Hiroshi Haeno, et al.. (2020). Combination treatment with a PI3K/Akt/mTOR pathway inhibitor overcomes resistance to anti-HER2 therapy in PIK3CA-mutant HER2-positive breast cancer cells. Scientific Reports. 10(1). 21762–21762. 61 indexed citations

Nakamura, Hiroko, Shigekazu Sasaki, Akio Matsushita, et al.. (2020). Liganded T3 receptor β2 inhibits the positive feedback autoregulation of the gene for GATA2, a transcription factor critical for thyrotropin production. PLoS ONE. 15(1). e0227646–e0227646. 5 indexed citations

Haeno, Hiroshi, Hiromitsu Komiyama, Harumi Saeki, et al.. (2019). Metastatic seeding of human colon cancer cell clusters expressing the hybrid epithelial/mesenchymal state. International Journal of Cancer. 146(9). 2547–2562. 39 indexed citations

10.

Stein, Shayna, Rui Zhao, Hiroshi Haeno, Igor Vivanco, & Franziska Michor. (2018). Mathematical modeling identifies optimum lapatinib dosing schedules for the treatment of glioblastoma patients. PLoS Computational Biology. 14(1). e1005924–e1005924. 31 indexed citations

11.

Yamamoto, Kimiyo N., Shinichi Yachida, Akira Nakamura, et al.. (2017). Personalized Management of Pancreatic Ductal Adenocarcinoma Patients through Computational Modeling. Cancer Research. 77(12). 3325–3335. 9 indexed citations

12.

Yamamoto, Kimiyo N., Masatsugu Ishii, Yoshihiro Inoué, et al.. (2016). Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model. Scientific Reports. 6(1). 34214–34214. 14 indexed citations

13.

Yamamoto, Kimiyo N., Akira Nakamura, & Hiroshi Haeno. (2015). The evolution of tumor metastasis during clonal expansion with alterations in metastasis driver genes. Scientific Reports. 5(1). 15886–15886. 15 indexed citations

14.

Yamamoto, Kimiyo N., Kouji Hirota, Shunichi Takeda, & Hiroshi Haeno. (2014). Evolution of Pre-Existing versus Acquired Resistance to Platinum Drugs and PARP Inhibitors in BRCA-Associated Cancers. PLoS ONE. 9(8). e105724–e105724. 9 indexed citations

15.

Haeno, Hiroshi, Mithat Gönen, Meghan B. Davis, et al.. (2012). Computational Modeling of Pancreatic Cancer Reveals Kinetics of Metastasis Suggesting Optimum Treatment Strategies. Cell. 148(1-2). 362–375. 306 indexed citations

16.

Iwami, Shingo, Hiroshi Haeno, & Franziska Michor. (2012). A Race between Tumor Immunoescape and Genome Maintenance Selects for Optimum Levels of (epi)genetic Instability. PLoS Computational Biology. 8(2). e1002370–e1002370. 7 indexed citations

17.

Hambardzumyan, Dolores, et al.. (2011). The Probable Cell of Origin of NF1- and PDGF-Driven Glioblastomas. PLoS ONE. 6(9). e24454–e24454. 26 indexed citations

18.

Klinakis, Apostolos, Camille Lobry, Omar Abdel‐Wahab, et al.. (2011). A novel tumour-suppressor function for the Notch pathway in myeloid leukaemia. Nature. 473(7346). 230–233. 271 indexed citations

19.

Haeno, Hiroshi & Franziska Michor. (2009). The evolution of tumor metastases during clonal expansion. Journal of Theoretical Biology. 263(1). 30–44. 27 indexed citations

20.

Haeno, Hiroshi & Yoh Iwasa. (2007). Probability of resistance evolution for exponentially growing virus in the host. Journal of Theoretical Biology. 246(2). 323–331. 7 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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