Daisuke Nakajima

1.5k total citations
47 papers, 1.1k citations indexed

About

Daisuke Nakajima is a scholar working on Molecular Biology, Spectroscopy and Physiology. According to data from OpenAlex, Daisuke Nakajima has authored 47 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 12 papers in Spectroscopy and 5 papers in Physiology. Recurrent topics in Daisuke Nakajima's work include Advanced Proteomics Techniques and Applications (10 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Mass Spectrometry Techniques and Applications (5 papers). Daisuke Nakajima is often cited by papers focused on Advanced Proteomics Techniques and Applications (10 papers), Metabolomics and Mass Spectrometry Studies (5 papers) and Mass Spectrometry Techniques and Applications (5 papers). Daisuke Nakajima collaborates with scholars based in Japan, United States and Australia. Daisuke Nakajima's co-authors include Osamu Ohara, Manabu Nakayama, Takahiro Nagase, Yusuke Kawashima, Hisashi Yamakawa, Naohiko Seki, Daisuke Sakai, Joji Mochida, Reiko Ohara and Masahiro Tanaka and has published in prestigious journals such as Applied and Environmental Microbiology, Macromolecules and Scientific Reports.

In The Last Decade

Daisuke Nakajima

46 papers receiving 1.1k citations

Peers

Daisuke Nakajima
Rong Zhao China
Yasuo Oda Japan
Sigrid A. Rajasekaran United States
K. Nagano Japan
John B. Shabb United States
Daisuke Nakajima
Citations per year, relative to Daisuke Nakajima Daisuke Nakajima (= 1×) peers Takehiro Yamamoto

Countries citing papers authored by Daisuke Nakajima

Since Specialization
Citations

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

Fields of papers citing papers by Daisuke Nakajima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daisuke Nakajima

This figure shows the co-authorship network connecting the top 25 collaborators of Daisuke Nakajima. A scholar is included among the top collaborators of Daisuke Nakajima 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 Daisuke Nakajima. Daisuke Nakajima 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.
Konno, Ryo, et al.. (2025). Thin-diaPASEF: diaPASEF for maximizing proteome coverage in single-shot proteomics. DNA Research. 32(4). 1 indexed citations
2.
Konno, Ryo, M. Ishikawa, Daisuke Nakajima, et al.. (2024). Universal Pretreatment Development for Low-input Proteomics Using Lauryl Maltose Neopentyl Glycol. Molecular & Cellular Proteomics. 23(4). 100745–100745. 10 indexed citations
3.
Muramatsu, Hideki, Hironori Sato, M. Ishikawa, et al.. (2024). Integrated proteogenomic analysis for inherited bone marrow failure syndrome. Leukemia. 38(6). 1256–1265.
4.
Kawashima, Yusuke, M. Ishikawa, Ryo Konno, Daisuke Nakajima, & Osamu Ohara. (2023). Development of a Simple and Stable NanoESI Spray System Using Suction Wind from the MS Inlet. Journal of Proteome Research. 22(5). 1564–1569. 5 indexed citations
5.
Sato, Hironori, Yuzaburo Inoue, Yusuke Kawashima, et al.. (2022). In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis. ACS Omega. 7(8). 7012–7023. 10 indexed citations
6.
Nakajima, Daisuke, et al.. (2021). A Simple Method for In-Depth Proteome Analysis of Mammalian Cell Culture Conditioned Media Containing Fetal Bovine Serum. International Journal of Molecular Sciences. 22(5). 2565–2565. 12 indexed citations
7.
Nakajima, Daisuke, Osamu Ohara, & Yusuke Kawashima. (2021). Data-Independent Acquisition Mass Spectrometry-Based Deep Proteome Analysis for Hydrophobic Proteins from Dried Blood Spots Enriched by Sodium Carbonate Precipitation. Methods in molecular biology. 2420. 39–52. 5 indexed citations
8.
Kitajima, Sakihito, Wataru Aoki, Daisuke Shibata, et al.. (2018). Comparative multi-omics analysis reveals diverse latex-based defense strategies against pests among latex-producing organs of the fig tree (Ficus carica). Planta. 247(6). 1423–1438. 26 indexed citations
9.
Kuwahara, Yusuke, Daisuke Nakajima, Sayaka Shinpo, et al.. (2018). Identification of potential genes involved in triterpenoid saponins biosynthesis in Gleditsia sinensis by transcriptome and metabolome analyses. Journal of Natural Medicines. 73(2). 369–380. 18 indexed citations
10.
Ito, Takashi, Koei Okazaki, Daisuke Nakajima, et al.. (2017). Mass spectrometry-based metabolomics to identify taurine-modified metabolites in heart. Amino Acids. 50(1). 117–124. 12 indexed citations
11.
Ara, Takeshi, Daisuke Nakajima, Kunihiro Suda, et al.. (2017). FlavonoidSearch: A system for comprehensive flavonoid annotation by mass spectrometry. Scientific Reports. 7(1). 1243–1243. 39 indexed citations
12.
Wang, Qingyue, et al.. (2014). Effects of Environmental Pollutants on Airborne Pollen Grains and the Behavior of Release Allergenic Species. 29(1). 1 indexed citations
13.
Hiyama, Akihiko, et al.. (2011). Effects of a glycogen synthase kinase-3β inhibitor (LiCl) on c-myc protein in intervertebral disc cells. Journal of Cellular Biochemistry. 112(10). 2974–2986. 32 indexed citations
14.
Hiyama, Akihiko, Daisuke Sakai, Masahiro Tanaka, et al.. (2010). The relationship between the Wnt/β‐catenin and TGF‐β/BMP signals in the intervertebral disc cell. Journal of Cellular Physiology. 226(5). 1139–1148. 63 indexed citations
15.
Nagase, Takahiro, Hisashi Yamakawa, S Tadokoro, et al.. (2008). Exploration of Human ORFeome: High-Throughput Preparation of ORF Clones and Efficient Characterization of Their Protein Products. DNA Research. 15(3). 137–149. 54 indexed citations
16.
Ozaki, Akiyuki, Takahiro Nagase, Ayako Watanabe, et al.. (2005). Utilization of mammalian cells for efficient and reliable evaluation of specificity of antibodies to unravel the cellular function of mKIAA proteins. Gene. 360(1). 35–44. 3 indexed citations
17.
Nakajima, Daisuke, Kazuki Saito, Hisashi Yamakawa, et al.. (2005). Preparation of a Set of Expression-Ready Clones of Mammalian Long cDNAs Encoding Large Proteins by the ORF Trap Cloning Method. DNA Research. 12(4). 257–267. 15 indexed citations
18.
Nakajima, Daisuke. (2002). Construction of Expression-ready cDNA Clones for KIAA Genes: Manual Curation of 330 KIAA cDNA Clones. DNA Research. 9(3). 99–106. 29 indexed citations
19.
Nakajima, Daisuke, Manabu Nakayama, Reiko Kikuno, et al.. (2001). Identification of three novel non-classical cadherin genes through comprehensive analysis of large cDNAs. Molecular Brain Research. 94(1-2). 85–95. 48 indexed citations
20.
Ohara, Osamu, Reiko Ohara, Hisashi Yamakawa, Daisuke Nakajima, & Manabu Nakayama. (1998). Characterization of a new β-spectrin gene which is predominantly expressed in brain. Molecular Brain Research. 57(2). 181–192. 58 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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