Masami Ikeda

1.5k total citations
73 papers, 1.2k citations indexed

About

Masami Ikeda is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Molecular Biology. According to data from OpenAlex, Masami Ikeda has authored 73 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Electrical and Electronic Engineering, 28 papers in Materials Chemistry and 22 papers in Molecular Biology. Recurrent topics in Masami Ikeda's work include High voltage insulation and dielectric phenomena (26 papers), Power Transformer Diagnostics and Insulation (24 papers) and Machine Learning in Bioinformatics (15 papers). Masami Ikeda is often cited by papers focused on High voltage insulation and dielectric phenomena (26 papers), Power Transformer Diagnostics and Insulation (24 papers) and Machine Learning in Bioinformatics (15 papers). Masami Ikeda collaborates with scholars based in Japan, Switzerland and Italy. Masami Ikeda's co-authors include Toshio Shimizu, Masafumi Arai, Masanobu Satake, Toru Kikuchi, H. Ōkubo, Masaki Honda, Yoshihiro Kawaguchi, Masaru Nonaka, Masanori Kasahara and Kaoru Azumi and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Scientific Reports.

In The Last Decade

Masami Ikeda

71 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Masami Ikeda Japan 16 512 259 242 208 84 73 1.2k
H. Li China 15 325 0.6× 99 0.4× 36 0.1× 68 0.3× 27 0.3× 52 1.1k
Christopher Osgood United States 18 521 1.0× 118 0.5× 562 2.3× 89 0.4× 6 0.1× 48 1.9k
Josef Chmelı́k Czechia 25 604 1.2× 76 0.3× 55 0.2× 55 0.3× 25 0.3× 123 1.9k
Xingwu Chen China 20 201 0.4× 80 0.3× 87 0.4× 106 0.5× 23 0.3× 108 1.2k
Dian Jiao China 22 772 1.5× 173 0.7× 175 0.7× 111 0.5× 5 0.1× 83 1.9k
Olga Zeni Italy 25 202 0.4× 264 1.0× 58 0.2× 34 0.2× 44 0.5× 67 1.6k
Xiaoqiong Li China 19 301 0.6× 83 0.3× 45 0.2× 58 0.3× 23 0.3× 82 990
Steffen Großmann Germany 9 558 1.1× 95 0.4× 50 0.2× 120 0.6× 24 0.3× 43 971
Yasuyuki Watanabe Japan 16 647 1.3× 430 1.7× 123 0.5× 30 0.1× 43 0.5× 101 1.5k
Jingwei Meng China 20 1.9k 3.8× 115 0.4× 605 2.5× 79 0.4× 8 0.1× 59 2.6k

Countries citing papers authored by Masami Ikeda

Since Specialization
Citations

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

Fields of papers citing papers by Masami Ikeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Masami Ikeda

This figure shows the co-authorship network connecting the top 25 collaborators of Masami Ikeda. A scholar is included among the top collaborators of Masami Ikeda 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 Masami Ikeda. Masami Ikeda 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.
Nagano, Nozomi, Masami Ikeda, Makoto Miwa, et al.. (2023). A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis. Scientific Reports. 13(1). 5986–5986.
2.
Ikeda, Kazuyoshi, et al.. (2021). PreBINDS: An Interactive Web Tool to Create Appropriate Datasets for Predicting Compound–Protein Interactions. Frontiers in Molecular Biosciences. 8. 758480–758480. 1 indexed citations
3.
Sohrab, Mohammad Golam, et al.. (2020). BENNERD: A Neural Named Entity Linking System for COVID-19. 182–188. 7 indexed citations
4.
Bandara, Wasala M.K.R.T.W., Masami Ikeda, Hisashi Satoh, et al.. (2013). Introduction of a Degassing Membrane Technology into Anaerobic Wastewater Treatment. Water Environment Research. 85(5). 387–390. 10 indexed citations
5.
Ikeda, Masami, et al.. (2013). Discrimination of Mammalian GPI-Anchored Proteins by Hydropathy and Amino Acid Propensities. Bioscience Biotechnology and Biochemistry. 77(3). 526–533. 3 indexed citations
6.
Yamada, Hidetaka, Kazuya Shinmura, Hiroaki Ito, et al.. (2011). Germline alterations in the CDH1 gene in familial gastric cancer in the Japanese population. Cancer Science. 102(10). 1782–1788. 32 indexed citations
7.
Deguchi, Kentaro, Syoichiro Kono, Shoko Deguchi, et al.. (2011). A patient with anti-aquaporin 4 antibody presenting hypersomnolence as the initial symptom and symmetrical hypothalamic lesions. Journal of the Neurological Sciences. 312(1-2). 18–20. 18 indexed citations
8.
Ikeda, Masami, et al.. (2004). ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability. Nucleic Acids Research. 32(Web Server). W390–W393. 173 indexed citations
9.
Ikeda, Masami, et al.. (2004). Proteome-wide classification and identification of mammalian-type GPCRs by binary topology pattern. Computational Biology and Chemistry. 28(1). 39–49. 29 indexed citations
10.
Ikeda, Masami, et al.. (2004). ConPred_elite: a highly reliable approach to transmembrane topology prediction. Computational Biology and Chemistry. 28(1). 51–60. 19 indexed citations
11.
Azumi, Kaoru, Rosaria De Santis, Anthony De Tomaso, et al.. (2003). Genomic analysis of immunity in a Urochordate and the emergence of the vertebrate immune system: “waiting for Godot”. Immunogenetics. 55(8). 570–581. 238 indexed citations
12.
Ikeda, Masami, et al.. (2003). Discharge Characteristics of SF_6 Gas for Very Fast Transient Voltage. 23(5). 363–368. 1 indexed citations
13.
Wang, Youjie, Jinping Song, Masami Ikeda, et al.. (2003). Ile-Leu Substitution (I415L) in Germline E-cadherin Gene (CDH1) in Japanese Familial Gastric Cancer. Japanese Journal of Clinical Oncology. 33(1). 17–20. 35 indexed citations
14.
Arai, Masafumi, et al.. (2002). The presence of signal peptide significantly affectstransmembrane topology prediction. Bioinformatics. 18(12). 1562–1566. 39 indexed citations
15.
Satō, Takashi, et al.. (2001). Structure Design of Neural Networks Using Genetic Algorithms.. Complex Systems. 13. 13 indexed citations
16.
Arai, Masafumi, et al.. (2001). Comprehensive Analysis of Transmembrane Protein Sequences in 39 Microbial Genomes. Proceedings Genome Informatics Workshop/Genome informatics. 12(12). 338–339. 1 indexed citations
17.
Sugiyama, Yoshiaki, et al.. (2001). Classification of Eukaryotic 7-tms Transmembrane Proteins by Binary Topology Pattern. Proceedings Genome Informatics Workshop/Genome informatics. 12. 336–337. 1 indexed citations
18.
Ikeda, Masami, Masafumi Arai, & Toshio Shimizu. (2000). Evaluation of Transmembrane Topology Prediction Methods by Using an Experimentally Characterized Topology Dataset. Proceedings Genome Informatics Workshop/Genome informatics. 11. 426–427. 3 indexed citations
19.
Oshima, Taku, et al.. (1993). [Detection of methicillin-resistant Staphylococcus aureus by in vitro enzymatic amplification of mecA and femA genes].. PubMed. 41(7). 773–8. 3 indexed citations
20.
Ikeda, Masami, et al.. (1977). DC Brekdown Probabilty and <i>V</i>-<i>t</i> Characteristics of Transfomer Oil. IEEJ Transactions on Power and Energy. 97(11). 715–721. 1 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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