Kazuhiro Maeda

457 total citations
30 papers, 313 citations indexed

About

Kazuhiro Maeda is a scholar working on Molecular Biology, Genetics and Plant Science. According to data from OpenAlex, Kazuhiro Maeda has authored 30 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 3 papers in Genetics and 3 papers in Plant Science. Recurrent topics in Kazuhiro Maeda's work include Gene Regulatory Network Analysis (19 papers), Microbial Metabolic Engineering and Bioproduction (15 papers) and Bioinformatics and Genomic Networks (5 papers). Kazuhiro Maeda is often cited by papers focused on Gene Regulatory Network Analysis (19 papers), Microbial Metabolic Engineering and Bioproduction (15 papers) and Bioinformatics and Genomic Networks (5 papers). Kazuhiro Maeda collaborates with scholars based in Japan, Netherlands and South Korea. Kazuhiro Maeda's co-authors include Hiroyuki Kurata, Yu Matsuoka, Fred C. Boogerd, Kentaro Inoue, Mikio Satake, Quanyu Zhao, TSUTOMU MIMURA, Hans V. Westerhoff, Takao Fujita and SHIGERU AONUMA and has published in prestigious journals such as Nucleic Acids Research, Environmental Science & Technology and PLoS ONE.

In The Last Decade

Kazuhiro Maeda

29 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kazuhiro Maeda Japan 12 205 27 23 23 19 30 313
Colin Kenyon South Africa 10 174 0.8× 18 0.7× 20 0.9× 24 1.0× 18 0.9× 19 313
Paromita Banerjee India 10 189 0.9× 45 1.7× 41 1.8× 29 1.3× 16 0.8× 24 393
Roshanak Aslebagh United States 13 277 1.4× 26 1.0× 36 1.6× 3 0.1× 20 1.1× 26 420
Sergio Rossell Netherlands 13 488 2.4× 51 1.9× 124 5.4× 21 0.9× 15 0.8× 18 574
Maxim A. Terpilowski Russia 7 106 0.5× 11 0.4× 13 0.6× 38 1.7× 13 0.7× 10 335
Joseph W. Scott United States 13 223 1.1× 25 0.9× 14 0.6× 23 1.0× 4 0.2× 16 353
Nanjiao Ying China 8 205 1.0× 71 2.6× 9 0.4× 17 0.7× 2 0.1× 23 325
Amoolya H. Singh United States 10 228 1.1× 35 1.3× 35 1.5× 22 1.0× 6 0.3× 13 358
Marie Le Berre Ireland 11 171 0.8× 13 0.5× 22 1.0× 11 0.5× 3 0.2× 19 300
Özgür Yürekten Türkiye 4 92 0.4× 11 0.4× 9 0.4× 12 0.5× 4 0.2× 7 218

Countries citing papers authored by Kazuhiro Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Kazuhiro Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazuhiro Maeda

This figure shows the co-authorship network connecting the top 25 collaborators of Kazuhiro Maeda. A scholar is included among the top collaborators of Kazuhiro Maeda 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 Kazuhiro Maeda. Kazuhiro Maeda 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.
Kurata, Hiroyuki, et al.. (2024). PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides. PLoS ONE. 19(8). e0309078–e0309078. 3 indexed citations
2.
3.
Maeda, Kazuhiro, et al.. (2023). Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach. Computers in Biology and Medicine. 169. 107848–107848. 12 indexed citations
4.
Maeda, Kazuhiro & Hiroyuki Kurata. (2023). Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT. International Journal of Molecular Sciences. 24(8). 7296–7296. 4 indexed citations
5.
Maeda, Kazuhiro, et al.. (2022). Simulation of the crosstalk between glucose and acetaminophen metabolism in a liver zonation model. Frontiers in Pharmacology. 13. 995597–995597.
6.
Maeda, Kazuhiro, et al.. (2022). MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling. BMC Bioinformatics. 23(1). 455–455. 8 indexed citations
7.
Maeda, Kazuhiro, Hans V. Westerhoff, Hiroyuki Kurata, & Fred C. Boogerd. (2019). Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network. npj Systems Biology and Applications. 5(1). 14–14. 17 indexed citations
8.
Maeda, Kazuhiro, Fred C. Boogerd, & Hiroyuki Kurata. (2018). libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models. Data Archiving and Networked Services (DANS). 11(0). 31–40. 5 indexed citations
9.
Yamamoto, Norihisa, Ryuji Kawahara, Yukihiro Akeda, et al.. (2017). Development of selective medium for IMP-type carbapenemase-producing Enterobacteriaceae in stool specimens. BMC Infectious Diseases. 17(1). 229–229. 18 indexed citations
10.
Maeda, Kazuhiro, et al.. (2017). Doing Bayesian Data Analysis. The Proceedings of the Annual Convention of the Japanese Psychological Association. 81(0). TWS–11. 29 indexed citations
11.
Maeda, Kazuhiro & Hiroyuki Kurata. (2017). Long negative feedback loop enhances period tunability of biological oscillators. Journal of Theoretical Biology. 440. 21–31. 6 indexed citations
12.
Inoue, Kentaro, et al.. (2014). CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis. Bioprocess and Biosystems Engineering. 37(9). 1925–1927. 6 indexed citations
13.
Maeda, Kazuhiro & Hiroyuki Kurata. (2014). Analytical study of robustness of a negative feedback oscillator by multiparameter sensitivity. BMC Systems Biology. 8(S5). S1–S1. 4 indexed citations
14.
Maeda, Kazuhiro & Hiroyuki Kurata. (2012). A Symmetric Dual Feedback System Provides a Robust and Entrainable Oscillator. PLoS ONE. 7(2). e30489–e30489. 11 indexed citations
15.
Maeda, Kazuhiro, et al.. (2012). Flux module decomposition for parameter estimation in a multiple-feedback loop model of biochemical networks. Bioprocess and Biosystems Engineering. 36(3). 333–344. 7 indexed citations
16.
Maeda, Kazuhiro, et al.. (2011). Biological Design Principles of Complex Feedback Modules in theE. coliAmmonia Assimilation System. Artificial Life. 18(1). 53–90. 12 indexed citations
17.
Maeda, Kazuhiro & Hiroyuki Kurata. (2010). Quasi-multiparameter sensitivity measure for robustness analysis of complex biochemical networks. Journal of Theoretical Biology. 272(1). 174–186. 9 indexed citations
18.
Maeda, Kazuhiro, et al.. (2010). An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks. Bioprocess and Biosystems Engineering. 34(4). 433–446. 12 indexed citations
19.
Maeda, Kazuhiro, et al.. (2008). A gradual update method for simulating the steady-state solution of stiff differential equations in metabolic circuits. Bioprocess and Biosystems Engineering. 32(2). 283–288. 2 indexed citations
20.
Kurata, Hiroyuki, et al.. (2007). Extended CADLIVE: a novel graphical notation for design of biochemical network maps and computational pathway analysis. Nucleic Acids Research. 35(20). e134–e134. 25 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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