Ken Aoshima

3.9k total citations
27 papers, 1.1k citations indexed

About

Ken Aoshima is a scholar working on Molecular Biology, Spectroscopy and Psychiatry and Mental health. According to data from OpenAlex, Ken Aoshima has authored 27 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 7 papers in Spectroscopy and 4 papers in Psychiatry and Mental health. Recurrent topics in Ken Aoshima's work include Metabolomics and Mass Spectrometry Studies (9 papers), Mass Spectrometry Techniques and Applications (6 papers) and Dementia and Cognitive Impairment Research (4 papers). Ken Aoshima is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (9 papers), Mass Spectrometry Techniques and Applications (6 papers) and Dementia and Cognitive Impairment Research (4 papers). Ken Aoshima collaborates with scholars based in Japan, United States and Sweden. Ken Aoshima's co-authors include Yoshiya Oda, François P. Bernier, Tatsuji Nakamura, Judy Oestreicher, Michael Byrne, Sergei Agoulnik, Pavan Kumar, Zoltán Dezső, Mamoru Yanagimachi and Crystal MacKenzie and has published in prestigious journals such as Bioinformatics, PLoS ONE and Analytical Chemistry.

In The Last Decade

Ken Aoshima

26 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ken Aoshima Japan 14 662 275 227 182 158 27 1.1k
Jong Hyuk Yoon South Korea 20 897 1.4× 205 0.7× 123 0.5× 123 0.7× 341 2.2× 44 1.5k
Helena Idborg Sweden 21 645 1.0× 146 0.5× 272 1.2× 114 0.6× 80 0.5× 57 1.3k
Thomas Büch Germany 18 661 1.0× 132 0.5× 109 0.5× 152 0.8× 151 1.0× 31 1.4k
Vitaly A. Selivanov Spain 19 1.3k 1.9× 282 1.0× 81 0.4× 106 0.6× 351 2.2× 52 2.0k
Parkyong Song South Korea 17 612 0.9× 112 0.4× 55 0.2× 118 0.6× 220 1.4× 38 1.2k
Andrew McBride United Kingdom 14 824 1.2× 221 0.8× 67 0.3× 91 0.5× 234 1.5× 20 1.4k
John Szpyt United States 12 1.1k 1.7× 216 0.8× 116 0.5× 158 0.9× 954 6.0× 13 2.1k
Zhiwei Yang China 19 778 1.2× 76 0.3× 117 0.5× 53 0.3× 269 1.7× 40 1.3k
Yunpeng Zhang China 23 929 1.4× 288 1.0× 24 0.1× 119 0.7× 102 0.6× 91 1.4k
David A. Liem United States 22 1.2k 1.8× 84 0.3× 263 1.2× 100 0.5× 181 1.1× 37 1.7k

Countries citing papers authored by Ken Aoshima

Since Specialization
Citations

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

Fields of papers citing papers by Ken Aoshima

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ken Aoshima

This figure shows the co-authorship network connecting the top 25 collaborators of Ken Aoshima. A scholar is included among the top collaborators of Ken Aoshima 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 Ken Aoshima. Ken Aoshima 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
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Kimura, Noriyuki, Yasuhiro Aso, Kotaro Sasaki, et al.. (2023). Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors. Alzheimer s Research & Therapy. 15(1). 212–212. 5 indexed citations
3.
Koyama, Noriyuki, et al.. (2023). Nationwide Database Analysis of Risk Factors Associated with Decreased Activities of Daily Living in Patients with Alzheimer’s Disease. Journal of Alzheimer s Disease. 94(4). 1465–1475. 2 indexed citations
4.
Koyama, Noriyuki, et al.. (2022). Nationwide database analysis of insomnia, depression, and sleeping pill prescriptions in hepatocellular carcinoma patients. Current Medical Research and Opinion. 38(8). 1333–1339. 5 indexed citations
5.
Koyama, Noriyuki, et al.. (2021). Real-world database analysis of the characteristics and treatment patterns of patients with endometrial cancer in Japan. Current Medical Research and Opinion. 37(7). 1171–1178. 8 indexed citations
6.
Yagi, Takuya, et al.. (2021). A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique. Neuroscience Research. 173. 99–105. 8 indexed citations
7.
Koyama, Noriyuki, et al.. (2019). Database analysis of patients with hepatocellular carcinoma and treatment flow in early and advanced stages. Pharmacology Research & Perspectives. 7(4). e00486–e00486. 15 indexed citations
8.
Yagi, Takuya, Michio Kanekiyo, Jun‐ichi Ito, et al.. (2019). Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study. Alzheimer s & Dementia Translational Research & Clinical Interventions. 5(1). 364–373. 13 indexed citations
9.
Sato, Yoshiaki, François P. Bernier, Ken Aoshima, et al.. (2015). Reduced plasma desmosterol‐to‐cholesterol ratio and longitudinal cognitive decline in Alzheimer's disease. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 1(1). 67–74. 7 indexed citations
10.
Funahashi, Yasuhiro, Kiyoshi Okamoto, Yusuke Adachi, et al.. (2014). Eribulin mesylate reduces tumor microenvironment abnormality by vascular remodeling in preclinical human breast cancer models. Cancer Science. 105(10). 1334–1342. 212 indexed citations
11.
Kajihara, Shigeki, et al.. (2014). Peptide Peak Detection for Low Resolution MALDI-TOF Mass Spectrometry. Mass Spectrometry. 3(1). A0030–A0030. 3 indexed citations
12.
Aoshima, Ken, Kentaro Takahashi, Takayuki Kimura, et al.. (2014). A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry. BMC Bioinformatics. 15(1). 376–376. 12 indexed citations
13.
Tanaka, Satoshi, et al.. (2014). Signal Processing Algorithm Development for Mass++ (Ver. 2): Platform Software for Mass Spectrometry. 7(0). 24–29. 5 indexed citations
14.
Shikata, Kohdoh, Mai Uesugi, Hiroyuki Katayama, et al.. (2013). Prediction of relaxin-3-induced downstream pathway resulting in anxiolytic-like behaviors in rats based on a microarray and peptidome analysis. Journal of Receptors and Signal Transduction. 33(4). 224–233. 21 indexed citations
15.
Agoulnik, Sergei, Judith Oestreicher, Noël Taylor, et al.. (2013). Abstract 3830: Eribulin and Paclitaxel differentially affect gene expression profiling of blood vessel cells and in vitro angiogenesis in co-cultures of human endothelial cells with pericytes.. Cancer Research. 73(8_Supplement). 3830–3830. 2 indexed citations
16.
Kumar, Pavan, Zoltán Dezső, Crystal MacKenzie, et al.. (2013). Circulating miRNA Biomarkers for Alzheimer's Disease. PLoS ONE. 8(7). e69807–e69807. 306 indexed citations
17.
Kumar, Pavan, Zoltán Dezső, Crystal MacKenzie, et al.. (2013). P1–234: Circulating miRNA biomarkers for Alzheimer's disease. Alzheimer s & Dementia. 9(4S_Part_6). 1 indexed citations
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Sato, Yoshiaki, et al.. (2011). Identification of a new plasma biomarker of Alzheimer's disease using metabolomics technology. Journal of Lipid Research. 53(3). 567–576. 120 indexed citations
20.
Myint, Khin Than, Taisuke Uehara, Ken Aoshima, & Yoshiya Oda. (2009). Polar Anionic Metabolome Analysis by Nano-LC/MS with a Metal Chelating Agent. Analytical Chemistry. 81(18). 7766–7772. 54 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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