Jo Nishino

558 total citations
30 papers, 335 citations indexed

About

Jo Nishino is a scholar working on Genetics, Molecular Biology and Oncology. According to data from OpenAlex, Jo Nishino has authored 30 papers receiving a total of 335 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Genetics, 9 papers in Molecular Biology and 6 papers in Oncology. Recurrent topics in Jo Nishino's work include Genetic Associations and Epidemiology (7 papers), Evolution and Genetic Dynamics (5 papers) and Genomics and Rare Diseases (3 papers). Jo Nishino is often cited by papers focused on Genetic Associations and Epidemiology (7 papers), Evolution and Genetic Dynamics (5 papers) and Genomics and Rare Diseases (3 papers). Jo Nishino collaborates with scholars based in Japan, United Kingdom and Qatar. Jo Nishino's co-authors include Hiroyuki Kagamiyama, Shunsuke Ishii, Shigeharu Wakana, Takeshi Yagi, Asao Fujiyama, Arikuni Uchimura, Yohei Minakuchi, Mizuki Ohno, Ikuo Miura and Atsushi Toyoda and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Jo Nishino

26 papers receiving 331 citations

Peers

Jo Nishino

GENET

BIOCH

Narayanan Raghupathy United States

Kevin Zhang United States

Asami Oji Japan

Huntington F. Willard United States

Malin Hernebring Sweden

Rafael A. Fissore United States

Yeonwoo Park United States

Harsha Rajasimha United States

Huiling Hao United States

M.T. Davisson Switzerland

Narayanan Raghupathy United States

Jo Nishino

415 ×2.3

236 ×1.7

GENET

77 ×1.9

50 ×1.4

9 ×0.3

BIOCH

Citations per year, relative to Jo Nishino Jo Nishino (= 1×) peers Narayanan Raghupathy

Countries citing papers authored by Jo Nishino

Since Specialization

Citations

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

Fields of papers citing papers by Jo Nishino

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jo Nishino

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

Umemura, Masanari, Tomoko Akiyama, Momoko Nagai, et al.. (2025). Focal Adhesion Kinase-Dependent Reprogramming of IFN-γ Signaling through PYK2 Coinhibition Sensitizes Melanoma to Immune Checkpoint Blockade. Journal of Investigative Dermatology. 1 indexed citations

Kato, Mamoru, Jo Nishino, Momoko Nagai, et al.. (2025). Comprehensive analysis of prognosis markers with molecular features derived from pan-cancer whole-genome sequences. Human Genomics. 19(1). 39–39.

Kamatani, Takashi, Fuyuki Miya, Kazuhiro Matsumoto, et al.. (2025). Clonal diversity shapes the tumour microenvironment leading to distinct immunotherapy responses in metastatic urothelial carcinoma. Nature Communications. 16(1). 7995–7995.

Nishino, Jo, Fuyuki Miya, & Mamoru Kato. (2025). Gene-based Hardy–Weinberg equilibrium test using genotype count data: application to six types of cancers. BMC Genomics. 26(1). 124–124. 1 indexed citations

Shibata, Seiko, N. Ikeda, Koichi Ito, et al.. (2024). POS0818 CHARACTERISTICS OF JAPANESE PATIENTS WITH SYSTEMIC SCLEROSIS WITH DYSPHAGIA A SINGLE-CENTER STUDY. Annals of the Rheumatic Diseases. 83. 1037–1037. 1 indexed citations

Kuzuya, Teiji, Yumi Ito, Jo Nishino, et al.. (2023). Polymyalgia rheumatica during combination therapy with atezolizumab plus bevacizumab for advanced hepatocellular carcinoma. Clinical Journal of Gastroenterology. 16(4). 567–571. 3 indexed citations

Sugawara, Toshitaka, Fuyuki Miya, Toshiaki Ishikawa, et al.. (2022). Immune subtypes and neoantigen-related immune evasion in advanced colorectal cancer. iScience. 25(2). 103740–103740. 8 indexed citations

Sugawara, Toshitaka, Daisuke Ban, Jo Nishino, et al.. (2021). Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection. PLoS ONE. 16(4). e0249885–e0249885. 14 indexed citations

Otani, Takahiro, et al.. (2021). Semi-parametric empirical Bayes factor for genome-wide association studies. European Journal of Human Genetics. 29(5). 800–807. 3 indexed citations

10.

Nishino, Jo, Hidenori Ochi, Yuta Kochi, Tatsuhiko Tsunoda, & Shigeyuki Matsui. (2018). Sample Size for Successful Genome-Wide Association Study of Major Depressive Disorder. Frontiers in Genetics. 9. 227–227. 27 indexed citations

11.

Otani, Takahiro, Hisashi Noma, Jo Nishino, & Shigeyuki Matsui. (2018). Re-assessment of multiple testing strategies for more efficient genome-wide association studies. European Journal of Human Genetics. 26(7). 1038–1048. 8 indexed citations

12.

Takahashi, Noriyuki, et al.. (2018). How many patients are required to provide a high level of reliability in the Japanese version of the CARE Measure? A secondary analysis. BMC Family Practice. 19(1). 138–138. 6 indexed citations

13.

Nishino, Jo, Yuta Kochi, Daichi Shigemizu, et al.. (2018). Empirical Bayes Estimation of Semi-parametric Hierarchical Mixture Models for Unbiased Characterization of Polygenic Disease Architectures. Frontiers in Genetics. 9. 115–115. 8 indexed citations

14.

Sugasawa, Shonosuke, Hisashi Noma, Takahiro Otani, Jo Nishino, & Shigeyuki Matsui. (2017). An efficient and flexible test for rare variant effects. European Journal of Human Genetics. 25(6). 752–757. 1 indexed citations

15.

Uchimura, Arikuni, Yohei Minakuchi, Mizuki Ohno, et al.. (2015). Germline mutation rates and the long-term phenotypic effects of mutation accumulation in wild-type laboratory mice and mutator mice. Genome Research. 25(8). 1125–1134. 114 indexed citations

16.

Katagiri, Satoshi, Takaaki Hayashi, Kazutoshi Yoshitake, et al.. (2015). Congenital Achromatopsia and Macular Atrophy Caused by a Novel RecessivePDE6CMutation (p.E591K). Ophthalmic Genetics. 36(2). 137–144. 6 indexed citations

17.

Katagiri, Satoshi, Takaaki Hayashi, Masakazu Akahori, et al.. (2014). RHOMutations (p.W126L and p.A346P) in Two Japanese Families with Autosomal Dominant Retinitis Pigmentosa. Journal of Ophthalmology. 2014. 1–10. 15 indexed citations

18.

Nishino, Jo, et al.. (2012). Stressors and rheumatoid arthritis: changes in stressors with advances in therapeutic agents. Rheumatology International. 33(4). 887–891. 2 indexed citations

19.

Nishino, Jo, Hiroaki Hayashi, Shunsuke Ishii, & Hiroyuki Kagamiyama. (1997). An Anomalous Side Reaction of the Lys303 Mutant Aromatic L- Amino Acid Decarboxylase Unravels the Role of the Residue in Catalysis. The Journal of Biochemistry. 121(3). 604–611. 21 indexed citations

20.

Ishii, Shunsuke, Hiroyuki Mizuguchi, Jo Nishino, Hideyuki Hayashi, & Hiroyuki Kagamiyama. (1996). Functionally Important Residues of Aromatic L-Amino Acid Decarboxylase Probed by Sequence Alignment and Site-Directed Mutagenesis. The Journal of Biochemistry. 120(2). 369–376. 40 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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