Yoko Machida

1.7k total citations · 1 hit paper
16 papers, 1.4k citations indexed

About

Yoko Machida is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Yoko Machida has authored 16 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 11 papers in Cellular and Molecular Neuroscience and 3 papers in Neurology. Recurrent topics in Yoko Machida's work include Genetic Neurodegenerative Diseases (11 papers), Mitochondrial Function and Pathology (5 papers) and Muscle Physiology and Disorders (3 papers). Yoko Machida is often cited by papers focused on Genetic Neurodegenerative Diseases (11 papers), Mitochondrial Function and Pathology (5 papers) and Muscle Physiology and Disorders (3 papers). Yoko Machida collaborates with scholars based in Japan, United Kingdom and United States. Yoko Machida's co-authors include Nobuyuki Nukina, Masaru Kurosawa, Motomasa Tanaka, Hiroshi Doi, Tetsurou Ikeda, Munenori Nekooki, Nihar Ranjan Jana, Sanyong Niu, Fumitaka Oyama and Yoshiyuki Kuroiwa and has published in prestigious journals such as Journal of Biological Chemistry, Nature Medicine and Biochemistry.

In The Last Decade

Yoko Machida

16 papers receiving 1.4k citations

Hit Papers

Trehalose alleviates polyglutamine-mediated pathology in ... 2004 2026 2011 2018 2004 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yoko Machida Japan 13 936 617 336 212 197 16 1.4k
Nobuhiro Fujikake Japan 22 1.3k 1.4× 746 1.2× 501 1.5× 264 1.2× 352 1.8× 30 1.9k
Sanyong Niu United States 11 758 0.8× 721 1.2× 172 0.5× 203 1.0× 302 1.5× 14 1.8k
Erkang Fei China 22 780 0.8× 415 0.7× 309 0.9× 209 1.0× 182 0.9× 46 1.3k
Tetsurou Ikeda Japan 9 489 0.5× 389 0.6× 189 0.6× 191 0.9× 164 0.8× 21 1.0k
Munenori Nekooki Japan 6 532 0.6× 435 0.7× 206 0.6× 185 0.9× 141 0.7× 6 877
Yanshan Fang China 21 895 1.0× 307 0.5× 224 0.7× 92 0.4× 96 0.5× 34 1.5k
Namita Agrawal India 20 1.0k 1.1× 689 1.1× 159 0.5× 92 0.4× 119 0.6× 63 1.6k
Shermali Gunawardena United States 18 1.2k 1.3× 897 1.5× 284 0.8× 106 0.5× 548 2.8× 40 2.0k
Carolanne E. Milligan United States 20 1.0k 1.1× 542 0.9× 194 0.6× 88 0.4× 263 1.3× 28 1.8k
Annie Sittler France 20 2.1k 2.2× 1.3k 2.1× 395 1.2× 111 0.5× 261 1.3× 29 2.4k

Countries citing papers authored by Yoko Machida

Since Specialization
Citations

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

Fields of papers citing papers by Yoko Machida

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoko Machida

This figure shows the co-authorship network connecting the top 25 collaborators of Yoko Machida. A scholar is included among the top collaborators of Yoko Machida 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 Yoko Machida. Yoko Machida is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Hayashi, Kumiko, Yoko Machida, Yuki Katayama, et al.. (2018). A case of antisynthetase syndrome with anti-EJ antibody complicated by pericarditis. Rinsho Shinkeigaku. 59(1). 21–26. 3 indexed citations
2.
Hattori, Yoshiyuki, et al.. (2016). Small interfering RNA delivery into the liver by cationic cholesterol derivative-based liposomes. Journal of Liposome Research. 27(4). 264–273. 14 indexed citations
3.
Hayashi, Yasunori, Yoshiki Ishii, Satoko Arai, et al.. (2016). Subacute sarcoid myositis with ocular muscle involvement; a case report and review of the literature.. PubMed. 33(3). 297–301. 6 indexed citations
4.
Hattori, Yoshiyuki, et al.. (2015). Optimization of siRNA Delivery Method into the Liver by Sequential Injection of Polyglutamic Acid and Cationic Lipoplex. Pharmacology & Pharmacy. 6(7). 302–310. 7 indexed citations
5.
Wong, Hon Kit, Peter Bauer, Masaru Kurosawa, et al.. (2008). Blocking acid-sensing ion channel 1 alleviates Huntington's disease pathology via an ubiquitin-proteasome system-dependent mechanism. Human Molecular Genetics. 17(20). 3223–3235. 104 indexed citations
6.
Doi, Hiroshi, Peter Bauer, Yoshiaki Furukawa, et al.. (2008). RNA-binding Protein TLS Is a Major Nuclear Aggregate-interacting Protein in Huntingtin Exon 1 with Expanded Polyglutamine-expressing Cells. Journal of Biological Chemistry. 283(10). 6489–6500. 102 indexed citations
7.
Machida, Yoko, Takashi Okada, Masaru Kurosawa, et al.. (2006). rAAV-mediated shRNA ameliorated neuropathology in Huntington disease model mouse. Biochemical and Biophysical Research Communications. 343(1). 190–197. 97 indexed citations
8.
Oyama, Fumitaka, Haruko Miyazaki, Naoaki Sakamoto, et al.. (2006). Sodium channel β4 subunit: down‐regulation and possible involvement in neuritic degeneration in Huntington's disease transgenic mice. Journal of Neurochemistry. 98(2). 518–529. 78 indexed citations
9.
Kotliarova, Svetlana, Nihar Ranjan Jana, Naoaki Sakamoto, et al.. (2005). Decreased expression of hypothalamic neuropeptides in Huntington disease transgenic mice with expanded polyglutamine‐EGFP fluorescent aggregates. Journal of Neurochemistry. 93(3). 641–653. 74 indexed citations
10.
Tanaka, Motomasa, Yoko Machida, & Nobuyuki Nukina. (2005). A novel therapeutic strategy for polyglutamine diseases by stabilizing aggregation-prone proteins with small molecules. Journal of Molecular Medicine. 83(5). 343–352. 61 indexed citations
11.
Tanaka, Motomasa, Yoko Machida, Sanyong Niu, et al.. (2004). Trehalose alleviates polyglutamine-mediated pathology in a mouse model of Huntington disease. Nature Medicine. 10(2). 148–154. 631 indexed citations breakdown →
12.
Doi, Hiroshi, Kenichi Mitsui, Masaru Kurosawa, et al.. (2004). Identification of ubiquitin‐interacting proteins in purified polyglutamine aggregates. FEBS Letters. 571(1-3). 171–176. 86 indexed citations
13.
Tanaka, Motomasa, Yoko Machida, Yukihiro Nishikawa, et al.. (2003). Expansion of Polyglutamine Induces the Formation of Quasi-aggregate in the Early Stage of Protein Fibrillization. Journal of Biological Chemistry. 278(36). 34717–34724. 46 indexed citations
14.
Tanaka, Motomasa, Yoko Machida, Yukihiro Nishikawa, et al.. (2002). The Effects of Aggregation-Inducing Motifs on Amyloid Formation of Model Proteins Related to Neurodegenerative Diseases. Biochemistry. 41(32). 10277–10286. 20 indexed citations
15.
Ono, Tamao & Yoko Machida. (1999). Immunomagnetic purification of viable primordial germ cells of Japanese quail (Coturnix japonica). Comparative Biochemistry and Physiology Part A Molecular & Integrative Physiology. 122(2). 255–259. 32 indexed citations
16.
Sato, Mitsutaka, et al.. (1996). In vivo Drug Release and Antitumor Characteristics of Water-Soluble Conjugates of Mitomycin C with Glycol-Chitosan and N-Succinyl-Chitosan.. Biological and Pharmaceutical Bulletin. 19(9). 1170–1177. 24 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026