Atsushi Niida

5.4k total citations
42 papers, 1.5k citations indexed

About

Atsushi Niida is a scholar working on Molecular Biology, Oncology and Cancer Research. According to data from OpenAlex, Atsushi Niida has authored 42 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 16 papers in Oncology and 16 papers in Cancer Research. Recurrent topics in Atsushi Niida's work include Cancer Genomics and Diagnostics (13 papers), Genetic factors in colorectal cancer (9 papers) and Bioinformatics and Genomic Networks (8 papers). Atsushi Niida is often cited by papers focused on Cancer Genomics and Diagnostics (13 papers), Genetic factors in colorectal cancer (9 papers) and Bioinformatics and Genomic Networks (8 papers). Atsushi Niida collaborates with scholars based in Japan, United States and Sweden. Atsushi Niida's co-authors include Tetsu Akiyama, Yutaka Suzuki, Satoru Miyano, Yoichi Furukawa, Yusuke Nakamura, Sumio Sugano, Koshi Mimori, Teppei Shimamura, Hiroyuki Aburatani and Seiya Imoto and has published in prestigious journals such as Journal of Clinical Oncology, Bioinformatics and PLoS ONE.

In The Last Decade

Atsushi Niida

40 papers receiving 1.4k citations

Peers

Atsushi Niida

ONCOL

GENET

PRM

Sara Akhavanfard United States

Sara A. Byron United States

Boris Winterhoff United States

Emmanuel Martínez-Ledesma United States

Nilgun Tasdemir United States

Kristian W. Pajtler Germany

Josephine Mun Yee Ko Hong Kong

Abdullah Mahmood Ali United States

Aldo Massimi United States

Victor Stastny United States

Sara Akhavanfard United States

Atsushi Niida

801 ×0.8

504 ×1.3

356 ×1.0

ONCOL

139 ×0.9

GENET

248 ×1.7

PRM

Citations per year, relative to Atsushi Niida Atsushi Niida (= 1×) peers Sara Akhavanfard

Countries citing papers authored by Atsushi Niida

Since Specialization

Citations

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

Fields of papers citing papers by Atsushi Niida

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Atsushi Niida

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

Nakano, Takafumi, Naoki Uno, Siew‐Kee Low, et al.. (2024). Implementable assay for monitoring minimum residual disease after radical treatment for colorectal cancer. Cancer Science. 115(6). 1989–2001. 2 indexed citations

Aiba, Tomoiki, Jun Sugisaka, Hisashi Shimizu, et al.. (2021). Gene expression signatures as candidate biomarkers of response to PD-1 blockade in non-small cell lung cancers. PLoS ONE. 16(11). e0260500–e0260500. 6 indexed citations

Yada, Erica, Rika Kasajima, Atsushi Niida, et al.. (2021). Possible Role of Cytochrome P450 1B1 in the Mechanism of Gemcitabine Resistance in Pancreatic Cancer. Biomedicines. 9(10). 1396–1396. 15 indexed citations

Niida, Atsushi, Koshi Mimori, Tatsuhiro Shibata, & Satoru Miyano. (2021). Modeling colorectal cancer evolution. Journal of Human Genetics. 66(9). 869–878. 18 indexed citations

Hasegawa, Takanori, Shuto Hayashi, Eigo Shimizu, et al.. (2020). Neoantimon: a multifunctional R package for identification of tumor-specific neoantigens. Bioinformatics. 36(18). 4813–4816. 9 indexed citations

Sato, Kuniaki, Takaaki Masuda, Qingjiang Hu, et al.. (2019). Novel oncogene 5MP1 reprograms c-Myc translation initiation to drive malignant phenotypes in colorectal cancer. EBioMedicine. 44. 387–402. 44 indexed citations

Niida, Atsushi, Takanori Hasegawa, & Satoru Miyano. (2019). Sensitivity analysis of agent-based simulation utilizing massively parallel computation and interactive data visualization. PLoS ONE. 14(3). e0210678–e0210678. 12 indexed citations

Niida, Atsushi, Satoshi Nagayama, Satoru Miyano, & Koshi Mimori. (2018). Understanding intratumor heterogeneity by combining genome analysis and mathematical modeling. Cancer Science. 109(4). 884–892. 23 indexed citations

Sato, Kuniaki, Atsushi Niida, Takaaki Masuda, et al.. (2018). Multiregion Genomic Analysis of Serially Transplanted Patient-derived Xenograft Tumors. Cancer Genomics & Proteomics. 16(1). 21–27. 10 indexed citations

10.

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

11.

Watanabe, Takaaki, Michael Marotta, Ryusuke Suzuki, et al.. (2017). Impediment of Replication Forks by Long Non-coding RNA Provokes Chromosomal Rearrangements by Error-Prone Restart. Cell Reports. 21(8). 2223–2235. 13 indexed citations

12.

Nakayama, Mizuho, Hiroko Oshima, Yuta Kouyama, et al.. (2017). Combined Mutation of Apc, Kras , and Tgfbr2 Effectively Drives Metastasis of Intestinal Cancer. Cancer Research. 78(5). 1334–1346. 94 indexed citations

13.

Matsui, Yusuke, Atsushi Niida, Ryutaro Uchi, et al.. (2017). phyC: Clustering cancer evolutionary trees. PLoS Computational Biology. 13(5). e1005509–e1005509. 8 indexed citations

14.

Hasegawa, Takanori, Atsushi Niida, Tomoya Mori, et al.. (2015). A likelihood-free filtering method via approximate Bayesian computation in evaluating biological simulation models. Computational Statistics & Data Analysis. 94. 63–74. 1 indexed citations

15.

Yamaguchi, Kiyoshi, Rui Yamaguchi, Norihiko Takahashi, et al.. (2014). Overexpression of Cohesion Establishment Factor DSCC1 through E2F in Colorectal Cancer. PLoS ONE. 9(1). e85750–e85750. 26 indexed citations

16.

Sawada, Genta, Yusuke Takahashi, Atsushi Niida, et al.. (2014). Loss of CDCP1 Expression Promotes Invasiveness and Poor Prognosis in Esophageal Squamous Cell Carcinoma. Annals of Surgical Oncology. 21(S4). 640–647. 14 indexed citations

17.

Shimamura, Teppei, Seiya Imoto, Yukako Shimada, et al.. (2011). A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition. PLoS ONE. 6(6). e20804–e20804. 34 indexed citations

18.

Niida, Atsushi, Seiya Imoto, Rui Yamaguchi, Masao Nagasaki, & Satoru Miyano. (2010). Gene Set-Based Module Discovery Decodes cis-Regulatory Codes Governing Diverse Gene Expression across Human Multiple Tissues. PLoS ONE. 5(6). e10910–e10910. 1 indexed citations

19.

Niida, Atsushi, Andrew D. Smith, Seiya Imoto, et al.. (2009). Gene set-based module discovery in the breast cancer transcriptome. BMC Bioinformatics. 10(1). 71–71. 19 indexed citations

20.

Torisu, Yuichi, Akira Watanabe, Aya Nonaka, et al.. (2008). Human homolog of NOTUM, overexpressed in hepatocellular carcinoma, is regulated transcriptionally by β‐catenin/TCF. Cancer Science. 99(6). 1139–1146. 46 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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