Eiko Shibata

692 total citations
16 papers, 564 citations indexed

About

Eiko Shibata is a scholar working on Molecular Biology, Oncology and Surgery. According to data from OpenAlex, Eiko Shibata has authored 16 papers receiving a total of 564 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 9 papers in Oncology and 3 papers in Surgery. Recurrent topics in Eiko Shibata's work include Lymphatic System and Diseases (4 papers), Angiogenesis and VEGF in Cancer (4 papers) and Cancer Cells and Metastasis (3 papers). Eiko Shibata is often cited by papers focused on Lymphatic System and Diseases (4 papers), Angiogenesis and VEGF in Cancer (4 papers) and Cancer Cells and Metastasis (3 papers). Eiko Shibata collaborates with scholars based in Japan and United States. Eiko Shibata's co-authors include Masaaki Shibata, Katsuhide Yoshidome, Minling Liu, Cheryl L. Jorcyk, Junji Morimoto, Yoshinori Otsuki, Alfonso Calvo, Miriam R. Anver, Christine Couldrey and Jon M. Wigginton and has published in prestigious journals such as The EMBO Journal, Oncogene and International Journal of Molecular Sciences.

In The Last Decade

Eiko Shibata

16 papers receiving 546 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eiko Shibata Japan	11	303	244	85	74	64	16	564
Sakunthala Muthugounder United States	15	319 1.1×	230 0.9×	89 1.0×	68 0.9×	200 3.1×	17	715
Kaiqun Ren China	20	561 1.9×	164 0.7×	152 1.8×	90 1.2×	106 1.7×	36	760
Hyun Ho Choi South Korea	19	777 2.6×	292 1.2×	227 2.7×	78 1.1×	59 0.9×	30	1.0k
Felicitas Mungenast Austria	9	147 0.5×	135 0.6×	98 1.2×	66 0.9×	47 0.7×	11	407
Ji-Hak Jeong South Korea	15	369 1.2×	151 0.6×	182 2.1×	24 0.3×	138 2.2×	24	684
Lieqi Liu Canada	9	546 1.8×	207 0.8×	89 1.0×	31 0.4×	88 1.4×	9	770
In‐Chul Park South Korea	9	237 0.8×	125 0.5×	106 1.2×	25 0.3×	58 0.9×	10	459
Su Jung Oh South Korea	13	472 1.6×	175 0.7×	199 2.3×	39 0.5×	80 1.3×	27	901
Uma Chandran India	9	347 1.1×	184 0.8×	273 3.2×	41 0.6×	54 0.8×	11	718

Countries citing papers authored by Eiko Shibata

Since Specialization

Citations

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

Fields of papers citing papers by Eiko Shibata

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eiko Shibata

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

All Works

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16 of 16 papers shown

Shibata, Masaaki, Eiko Shibata, Junji Morimoto, & Yoichi Kondo. (2020). Urethane-induced Mammary Carcinogenesis Susceptibility in Transgenic Mice Expressing a Dominant-negative TGF-β Type II Receptor. Anticancer Research. 40(5). 2687–2694. 1 indexed citations

Shibata, Masaaki, et al.. (2020). Soluble Vegfr3 gene therapy suppresses multi‐organ metastasis in a mouse mammary cancer model. Cancer Science. 111(8). 2837–2849. 10 indexed citations

Shibata, Masaaki, Mariko Harada‐Shiba, Eiko Shibata, et al.. (2019). Crude α-Mangostin Suppresses the Development of Atherosclerotic Lesions in Apoe-Deficient Mice by a Possible M2 Macrophage-Mediated Mechanism. International Journal of Molecular Sciences. 20(7). 1722–1722. 12 indexed citations

Shibata, Masaaki, Eiko Shibata, Kentaro Maemura, Yoichi Kondo, & Mariko Harada‐Shiba. (2017). Pathological and molecular analyses of atherosclerotic lesions in ApoE-knockout mice. Medical Molecular Morphology. 50(3). 130–144. 15 indexed citations

Shibata, Masaaki, Eiko Shibata, S Fujioka, & Mariko Harada‐Shiba. (2015). Apolipoprotein E-knockout Mice as a Lifestyle-related Disease Model of Atherosclerosis and Non-alcoholic Fatty Liver Disease. 1(2). 5 indexed citations

Yamamoto, Tsuyoshi, Satoshi Obika, Hidenori Yasuhara, et al.. (2013). Locked nucleic acid antisense inhibitor targeting apolipoprotein C-III efficiently and preferentially removes triglyceride from large very low-density lipoprotein particles in murine plasma. European Journal of Pharmacology. 723. 353–359. 10 indexed citations

Shibata, Masaaki, Eiko Shibata, Junji Morimoto, & Mariko Harada‐Shiba. (2013). Therapy with siRNA for Vegf-c but not for Vegf-d suppresses wide-spectrum organ metastasis in an immunocompetent xenograft model of metastatic mammary cancer.. PubMed. 33(10). 4237–47. 10 indexed citations

Shibata, Masaaki, Jayakrishna Ambati, Eiko Shibata, Katsuhide Yoshidome, & Mariko Harada‐Shiba. (2012). Mammary cancer gene therapy targeting lymphangiogenesis: VEGF-C siRNA and soluble VEGF receptor-2, a splicing variant. Medical Molecular Morphology. 45(4). 179–184. 9 indexed citations

Shibata, Masaaki, Junji Morimoto, Eiko Shibata, et al.. (2010). Raloxifene inhibits tumor growth and lymph node metastasis in a xenograft model of metastatic mammary cancer. BMC Cancer. 10(1). 566–566. 14 indexed citations

10.

Shibata, Masaaki, Jayakrishna Ambati, Eiko Shibata, et al.. (2010). The endogenous soluble VEGF receptor-2 isoform suppresses lymph node metastasis in a mouse immunocompetent mammary cancer model. BMC Medicine. 8(1). 69–69. 16 indexed citations

11.

Shibata, Masaaki, Eiko Shibata, Junji Morimoto, et al.. (2009). Panaxanthone isolated from pericarp of Garcinia mangostana L. suppresses tumor growth and metastasis of a mouse model of mammary cancer.. PubMed. 29(7). 2485–95. 45 indexed citations

12.

Shibata, Masaaki, Eiko Shibata, Junji Morimoto, et al.. (2009). An immunocompetent murine model of metastatic mammary cancer accessible to bioluminescence imaging.. PubMed. 29(11). 4389–95. 20 indexed citations

13.

Shibata, Masaaki, Yukihiro Akao, Eiko Shibata, et al.. (2007). Vaticanol C, a novel resveratrol tetramer, reduces lymph node and lung metastases of mouse mammary carcinoma carrying p53 mutation. Cancer Chemotherapy and Pharmacology. 60(5). 681–691. 36 indexed citations

14.

Shibata, Masaaki, et al.. (2001). Suppression of mammary carcinoma growth in vitro and in vivo by inducible expression of the Cdk inhibitor p21. Cancer Gene Therapy. 8(1). 23–35. 52 indexed citations

15.

Shibata, Masaaki, Katsuhide Yoshidome, Minling Liu, et al.. (2000). The C3(1)/SV40 T-antigen transgenic mouse model of mammary cancer: ductal epithelial cell targeting with multistage progression to carcinoma. Oncogene. 19(8). 1020–1027. 204 indexed citations

16.

Shibata, Masaaki, Minling Liu, Michael Knudson, et al.. (1999). Haploid loss of bax leads to accelerated mammary tumor development in C3(1)/SV40-TAg transgenic mice: reduction in protective apoptotic response at the preneoplastic stage. The EMBO Journal. 18(10). 2692–2701. 105 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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