S Takayama

863 total citations
8 papers, 736 citations indexed

About

S Takayama is a scholar working on Molecular Biology, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, S Takayama has authored 8 papers receiving a total of 736 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 2 papers in Surgery and 2 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in S Takayama's work include Metabolism, Diabetes, and Cancer (6 papers), Protein Kinase Regulation and GTPase Signaling (2 papers) and Diabetes and associated disorders (2 papers). S Takayama is often cited by papers focused on Metabolism, Diabetes, and Cancer (6 papers), Protein Kinase Regulation and GTPase Signaling (2 papers) and Diabetes and associated disorders (2 papers). S Takayama collaborates with scholars based in United States, Japan and Canada. S Takayama's co-authors include Morris F. White, C. Ronald Kahn, C. Ronald Kahn, Vilma Lauris, Katsumaro Kubo, James E. Foley, Yoko Eguchi, Yukimasa Hirata, Taro Wasada and Kenshi Yao and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Journal of Clinical Endocrinology & Metabolism.

In The Last Decade

S Takayama

7 papers receiving 692 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
S Takayama United States	6	564	208	169	143	62	8	736
O. Sonne Denmark	14	473 0.8×	203 1.0×	158 0.9×	264 1.8×	110 1.8×	25	847
B Ermel Germany	13	620 1.1×	247 1.2×	220 1.3×	195 1.4×	88 1.4×	14	805
R W Rees-Jones United States	14	648 1.1×	240 1.2×	294 1.7×	89 0.6×	74 1.2×	16	891
K T Yu United States	12	628 1.1×	183 0.9×	168 1.0×	130 0.9×	116 1.9×	21	781
Angela Roncone United States	10	238 0.4×	138 0.7×	128 0.8×	92 0.6×	75 1.2×	16	487
Gyohan Koh Japan	9	306 0.5×	121 0.6×	86 0.5×	79 0.6×	23 0.4×	16	612
Yasutaka Ota Japan	8	235 0.4×	180 0.9×	65 0.4×	103 0.7×	41 0.7×	9	585
Christine L. Webb United States	11	260 0.5×	220 1.1×	51 0.3×	74 0.5×	34 0.5×	16	643
S Kim Japan	14	361 0.6×	97 0.5×	205 1.2×	85 0.6×	24 0.4×	22	817
Karin Conde‐Knape United States	14	240 0.4×	131 0.6×	213 1.3×	98 0.7×	62 1.0×	27	686

Countries citing papers authored by S Takayama

Since Specialization

Citations

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

Fields of papers citing papers by S Takayama

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S Takayama

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

All Works

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8 of 8 papers shown

1.

Kobayashi, Isao, et al.. (2008). Utility of multi focus virtual slide in cytology. The Journal of the Japanese Society of Clinical Cytology. 47(1). 25–28.

2.

Takayama, S, et al.. (1999). Dynamic control of the release of a hepatic insulin-sensitizing substance.. PubMed. 42. 63–4. 4 indexed citations

3.

Wasada, Taro, et al.. (1989). Insulin Autoimmune Syndrome Associated With Benign Monoclonal Gammopathy: Evidence for Monoclonal Insulin Autoantibodies. Diabetes Care. 12(2). 147–150. 28 indexed citations

4.

Wasada, Taro, Yoko Eguchi, S Takayama, Kenshi Yao, & Yukimasa Hirata. (1988). Reverse Phase High Performance Liquid Chromatographic Analysis of Circulating Insulin in the Insulin Autoimmune Syndrome. The Journal of Clinical Endocrinology & Metabolism. 66(1). 153–158. 14 indexed citations

5.

Takayama, S, C. Ronald Kahn, Katsumaro Kubo, & James E. Foley. (1988). Alterations in Insulin Receptor Autophosphorylation in Insulin Resistance: Correlation With Altered Sensitivity to Glucose Transport and Antilipolysis to Insulin*. The Journal of Clinical Endocrinology & Metabolism. 66(5). 992–999. 36 indexed citations

6.

Takayama, S, Morris F. White, & C. Ronald Kahn. (1988). Phorbol ester-induced serine phosphorylation of the insulin receptor decreases its tyrosine kinase activity.. Journal of Biological Chemistry. 263(7). 3440–3447. 286 indexed citations

7.

White, Morris F., S Takayama, & C. Ronald Kahn. (1985). Differences in the sites of phosphorylation of the insulin receptor in vivo and in vitro.. Journal of Biological Chemistry. 260(16). 9470–9478. 134 indexed citations

8.

Takayama, S, Morris F. White, Vilma Lauris, & C. Ronald Kahn. (1984). Phorbol esters modulate insulin receptor phosphorylation and insulin action in cultured hepatoma cells.. Proceedings of the National Academy of Sciences. 81(24). 7797–7801. 234 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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