Akiko Ando

37 papers receiving 1.4k citations

Peers

Akiko Ando
Comparison fields: 5 of 102
  • Hepatology 133
  • Cancer Research 177
  • Genetics 314
  • Pharmacology 96
  • Molecular Biology 646
Replace Masayoshi Kanisawa with:
Masayoshi Kanisawa Japan
Pierpaolo Coni Italy
Chang-Fang Chiu Taiwan
Marco A. De Velasco Japan
Maria R. De Miglio Italy
Charles S. Morrow United States
Yoshitaka Miyakawa Japan
Jisong Cui United States
Xiao-bo Zhong United States
Yoshiteru Kitahori Japan
Akiko Ando relative to Masayoshi Kanisawa Japan Masayoshi Kanisawa's profile →
Citations per field
00.5×2×3×3.8×
Masayoshi Kanisawa · 1×
Citations per year

Countries citing papers authored by Akiko Ando

Since Specialization
Citations

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

Fields of papers citing papers by Akiko Ando

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Akiko Ando, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Akiko Ando Line = papers co-authored together Akiko Ando links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2005257
2
High expression of Wilms' tumor suppressor gene predicts poor prognosis in breast cancer patients.
2002173
3 2001135
4 1995125
5 200194
6
High expression of steroid sulfatase mRNA predicts poor prognosis in patients with estrogen receptor-positive breast cancer.
200393
7 198758
8 200345
9 198845
10 200843
11 200341
12
Association of BRCA2 polymorphism at codon 784 (Met/Val) with breast cancer risk and prognosis.
200335
13 199334
14 199234
15 197528
16 198327
17 199526
18 200120
19
Effects of dietary protein intake on renal function in humans.
198920
20 200216

About Akiko Ando

Akiko Ando is a scholar working on Organic Chemistry, Molecular Biology, Pulmonary and Respiratory Medicine, Genetics and Surgery, having authored 41 papers that have together received 1.5k indexed citations. Recurring topics across this work include Synthesis of β-Lactam Compounds (5 papers), Estrogen and related hormone effects (4 papers), Animal Virus Infections Studies (3 papers), Renal and related cancers (2 papers), Antibiotics Pharmacokinetics and Efficacy (2 papers), Monoclonal and Polyclonal Antibodies Research (2 papers), Hepatitis C virus research (2 papers) and Antibiotic Resistance in Bacteria (2 papers). The work is most often cited by research in Hepatology (133 citations), Cancer Research (177 citations), Genetics (314 citations), Pharmacology (96 citations) and Molecular Biology (646 citations). Akiko Ando has collaborated with scholars based in Japan, United Kingdom and Germany. Frequent co-authors include Yasuo Miyoshi, Shinzaburo Noguchi, Yasuhiro Tamaki, Tetsuya Taguchi, Hiroaki Yanagisawa, Kyoko Iwao‐Koizumi, Ryo Matoba, Noriko Ueno, Seung Jin Kim and Kikuya Kato. Their work appears in journals such as Tetrahedron Letters, Journal of Medicinal Chemistry, International Journal of Cancer, Journal of General Virology and The Journal of Antibiotics.

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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