Daisuke Tominaga

1.1k total citations
35 papers, 814 citations indexed

About

Daisuke Tominaga is a scholar working on Molecular Biology, Artificial Intelligence and Numerical Analysis. According to data from OpenAlex, Daisuke Tominaga has authored 35 papers receiving a total of 814 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 5 papers in Artificial Intelligence and 4 papers in Numerical Analysis. Recurrent topics in Daisuke Tominaga's work include Gene Regulatory Network Analysis (7 papers), Bioinformatics and Genomic Networks (7 papers) and Microbial Metabolic Engineering and Bioproduction (7 papers). Daisuke Tominaga is often cited by papers focused on Gene Regulatory Network Analysis (7 papers), Bioinformatics and Genomic Networks (7 papers) and Microbial Metabolic Engineering and Bioproduction (7 papers). Daisuke Tominaga collaborates with scholars based in Japan. Daisuke Tominaga's co-authors include Katsutoshi Takahashi, Masahiro Okamoto, Masaru Tomita, Shinichi Kikuchi, Masanori Arita, Yasuyuki Maki, S. WATANABE, Paul Horton, Yoshihiko Yoshii and Akio Hyodo and has published in prestigious journals such as Bioinformatics, PLoS ONE and BMC Bioinformatics.

In The Last Decade

Daisuke Tominaga

34 papers receiving 776 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daisuke Tominaga Japan 12 559 186 99 54 49 35 814
Yamashita Japan 17 87 0.2× 84 0.5× 17 0.2× 36 0.7× 34 0.7× 53 725
Richa Batra Germany 15 368 0.7× 41 0.2× 34 0.3× 33 0.6× 7 0.1× 30 813
Jakub Tomek United Kingdom 16 399 0.7× 95 0.5× 45 0.5× 20 0.4× 4 0.1× 37 1.1k
Parminder Singh Reel United Kingdom 8 304 0.5× 39 0.2× 57 0.6× 21 0.4× 6 0.1× 16 649
Asif Javed United States 13 215 0.4× 78 0.4× 207 2.1× 48 0.9× 12 0.2× 43 733
Benjamin Ulfenborg Sweden 10 193 0.3× 124 0.7× 23 0.2× 17 0.3× 8 0.2× 26 560
Hui-Ling Huang Taiwan 16 402 0.7× 45 0.2× 13 0.1× 54 1.0× 5 0.1× 26 730
Jiangwen Sun United States 14 276 0.5× 146 0.8× 136 1.4× 4 0.1× 11 0.2× 33 722
Andrea Edwards United States 14 131 0.2× 144 0.8× 36 0.4× 5 0.1× 11 0.2× 45 714
Zhixiang Li China 13 161 0.3× 38 0.2× 8 0.1× 16 0.3× 39 0.8× 67 588

Countries citing papers authored by Daisuke Tominaga

Since Specialization
Citations

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

Fields of papers citing papers by Daisuke Tominaga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daisuke Tominaga

This figure shows the co-authorship network connecting the top 25 collaborators of Daisuke Tominaga. A scholar is included among the top collaborators of Daisuke Tominaga 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 Tominaga. Daisuke Tominaga 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.
2.
Nagai‐Okatani, Chiaki, et al.. (2024). Inter-tissue glycan heterogeneity: site-specific glycoform analysis of mouse tissue N-glycoproteomes using MS1-based glycopeptide detection method assisted by lectin microarray. Analytical and Bioanalytical Chemistry. 417(5). 973–988. 3 indexed citations
3.
Tominaga, Daisuke. (2021). Logarithmic Quadratic Regression Model for Early Periods of COVID-19 Epidemic Count Data. Archives of Clinical and Biomedical Research. 5(5).
4.
Yamazaki, Miki, Koji Arikawa, Masahito Hosokawa, et al.. (2020). Cortical transcriptome analysis after spinal cord injury reveals the regenerative mechanism of central nervous system in CRMP2 knock-in mice. Neural Regeneration Research. 16(7). 1258–1258. 11 indexed citations
5.
Tateyama, Masao, Takeshi Kinjo, Gretchen Parrott, et al.. (2018). Comparison of two screening tests for HIV-Associated Neurocognitive Disorder suspected Japanese patients with respect to cART usage. PLoS ONE. 13(6). e0199106–e0199106. 7 indexed citations
6.
Tominaga, Daisuke, et al.. (2016). Linear and Nonlinear Regression for Combinatorial Optimization Problem of Multiple Transgenesis. 9(0). 7–11. 1 indexed citations
7.
Nishimura, Masahiko, Takashi Watanabe, Sadayuki Murayama, et al.. (2015). Cerebellar Contribution to Pattern Separation of Human Hippocampal Memory Circuits. The Cerebellum. 15(6). 645–662. 12 indexed citations
8.
Shimoji, Takeyoshi, et al.. (2014). Analysis of pre- and post-operative symptoms of patients with mild trigonocephaly using several developmental and psychological tests. Child s Nervous System. 31(3). 433–440. 17 indexed citations
9.
Tominaga, Daisuke, Kyoko Miyagi, Hideta Nakamura, et al.. (2014). Are MMSE and HDS-R neuropsychological tests adequate for screening HIV-associated neurocognitive disorders?. Journal of Infection and Chemotherapy. 20(3). 217–219. 4 indexed citations
10.
Tominaga, Daisuke, et al.. (2013). Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset. BMC Genomics. 14(S2). S9–S9. 5 indexed citations
11.
Tominaga, Daisuke. (2010). Periodicity Detection Method for Small-Sample Time Series Datasets. Bioinformatics and Biology Insights. 4. BBI.S5983–BBI.S5983. 6 indexed citations
12.
Yoshii, Yoshihiko, et al.. (2007). Cognitive function of patients with brain tumor in pre- and postoperative stage. Surgical Neurology. 69(1). 51–61. 61 indexed citations
13.
Kikuchi, Shinichi, Daisuke Tominaga, Masanori Arita, Katsutoshi Takahashi, & Masaru Tomita. (2003). Dynamic modeling of genetic networks using genetic algorithmand S-system. Bioinformatics. 19(5). 643–650. 304 indexed citations
14.
Kikuchi, Shinichi, Daisuke Tominaga, Masanori Arita, & Masaru Tomita. (2001). Pathway Finding from Given Time-Courses Using Genetic Algorithm. Proceedings Genome Informatics Workshop/Genome informatics. 12. 304–305. 6 indexed citations
15.
Tominaga, Daisuke, et al.. (2000). Efficient numerical optimization algorithm based on Genetic Algorithm for inverse problem. Genetic and Evolutionary Computation Conference. 251–258. 63 indexed citations
16.
Tominaga, Daisuke, et al.. (1999). Nonlinear Numerical Optimization Technique Based on a Genetic Algorithm for Inverse Problems: Towards the Inference of Genetic Networks. 127–140. 17 indexed citations
17.
Ogawa, Kazuhiko, Yasumasa Kakinohana, Daisuke Tominaga, et al.. (1999). A patient with improvement in short-term memory disturbance brought about by radiation therapy for germinoma involving Papez circuit.. PubMed. 17(4). 317–22. 5 indexed citations
18.
Tominaga, Daisuke & Masahiro Okamoto. (1999). Nonlinear Numerical Optimization Technique Based on Genetic Algorithm for Inverse Problem.. KAGAKU KOGAKU RONBUNSHU. 25(2). 220–225. 6 indexed citations
19.
Tominaga, Daisuke & Masahiro Okamoto. (1998). Design of Canonical Model Describing Complex Nonlinear Dynamics. IFAC Proceedings Volumes. 31(8). 85–90. 13 indexed citations
20.
Okamoto, Masahiro, Daisuke Tominaga, Kouji Tanaka, et al.. (1997). Toward a virtual-labo-system for metabolic engineering: development of biochemical engineering system analyzing tool-kit (BEST-KIT).. PubMed. 304–15. 3 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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