Mingda Yan

987 total citations
19 papers, 802 citations indexed

About

Mingda Yan is a scholar working on Molecular Biology, Cell Biology and Immunology. According to data from OpenAlex, Mingda Yan has authored 19 papers receiving a total of 802 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Molecular Biology, 5 papers in Cell Biology and 4 papers in Immunology. Recurrent topics in Mingda Yan's work include Peroxisome Proliferator-Activated Receptors (5 papers), Sphingolipid Metabolism and Signaling (3 papers) and Caveolin-1 and cellular processes (3 papers). Mingda Yan is often cited by papers focused on Peroxisome Proliferator-Activated Receptors (5 papers), Sphingolipid Metabolism and Signaling (3 papers) and Caveolin-1 and cellular processes (3 papers). Mingda Yan collaborates with scholars based in United States, South Korea and Italy. Mingda Yan's co-authors include Suresh Subramani, Naganand Rayapuram, Zhongcheng Zheng, Xinyuan Liu, Lanying Sun, Weijing Xu, Weiguo Zou, Hairong Huo, Danny N. Dhanasekaran and Muralidharan Jayaraman and has published in prestigious journals such as The Journal of Cell Biology, Molecular and Cellular Biology and Cancer Research.

In The Last Decade

Mingda Yan

19 papers receiving 794 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mingda Yan United States	12	589	144	103	99	74	19	802
Brian Cunniff United States	14	582 1.0×	134 0.9×	91 0.9×	122 1.2×	55 0.7×	28	915
Wei Shao United States	14	532 0.9×	267 1.9×	102 1.0×	82 0.8×	78 1.1×	24	924
Marcus Höring Germany	17	474 0.8×	197 1.4×	117 1.1×	45 0.5×	55 0.7×	61	852
Marta Benet Spain	13	633 1.1×	125 0.9×	273 2.7×	60 0.6×	77 1.0×	26	1.1k
Qinghua Wang China	14	693 1.2×	173 1.2×	79 0.8×	99 1.0×	44 0.6×	49	946
Shinjinee Sengupta India	12	623 1.1×	74 0.5×	81 0.8×	53 0.5×	65 0.9×	35	952
Sue Young Oh South Korea	9	451 0.8×	54 0.4×	151 1.5×	86 0.9×	132 1.8×	10	691
Sun-Yee Kim South Korea	15	416 0.7×	98 0.7×	57 0.6×	33 0.3×	42 0.6×	27	726
Akito Tomomura Japan	18	508 0.9×	65 0.5×	82 0.8×	72 0.7×	56 0.8×	51	1.0k

Countries citing papers authored by Mingda Yan

Since Specialization

Citations

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

Fields of papers citing papers by Mingda Yan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mingda Yan

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

All Works

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19 of 19 papers shown

Ha, Ji Hee, Rangasudhagar Radhakrishnan, Revathy Nadhan, et al.. (2024). Deciphering a GPCR-lncrna-miRNA nexus: Identification of an aberrant therapeutic target in ovarian cancer. Cancer Letters. 591. 216891–216891. 8 indexed citations

Ha, Ji Hee, Muralidharan Jayaraman, Mingda Yan, et al.. (2021). GNAi2/gip2-Regulated Transcriptome and Its Therapeutic Significance in Ovarian Cancer. Biomolecules. 11(8). 1211–1211. 11 indexed citations

Jayaraman, Muralidharan, et al.. (2021). Identification of GNA12‑driven gene signatures and key signaling networks in ovarian cancer. Oncology Letters. 22(4). 719–719. 6 indexed citations

Ha, Ji Hee, Muralidharan Jayaraman, Rangasudhagar Radhakrishnan, et al.. (2020). Differential effects of thymoquinone on lysophosphatidic acid-induced oncogenic pathways in ovarian cancer cells. Journal of Traditional and Complementary Medicine. 10(3). 207–216. 14 indexed citations

Ha, Ji Hee, Rangasudhagar Radhakrishnan, Muralidharan Jayaraman, et al.. (2018). LPA Induces Metabolic Reprogramming in Ovarian Cancer via a Pseudohypoxic Response. Cancer Research. 78(8). 1923–1934. 68 indexed citations

Jayaraman, Muralidharan, Rangasudhagar Radhakrishnan, Cara Mathews, et al.. (2017). Identification of novel diagnostic and prognostic miRNA signatures in endometrial cancer. Genes & Cancer. 8(5-6). 566–576. 46 indexed citations

Ha, Ji Hee, Mingda Yan, Rohini Gomathinayagam, et al.. (2016). Aberrant expression of JNK-associated leucine-zipper protein, JLP, promotes accelerated growth of ovarian cancer. Oncotarget. 7(45). 72845–72859. 13 indexed citations

Yan, Mingda, Ji Hee Ha, & Danny N. Dhanasekaran. (2015). Gα13 Stimulates the Tyrosine Phosphorylation of Ric-8A. PubMed. 10. 3–3. 5 indexed citations

Ha, Ji Hee, Rohini Gomathinayagam, Mingda Yan, et al.. (2015). Determinant role for the gep oncogenes, Gα12/13, in ovarian cancer cell proliferation and xenograft tumor growth. Genes & Cancer. 6(7-8). 356–364. 14 indexed citations

10.

Nazarko, Taras Y., et al.. (2014). Peroxisomal Atg37 binds Atg30 or palmitoyl-CoA to regulate phagophore formation during pexophagy. The Journal of Cell Biology. 204(4). 541–557. 84 indexed citations

11.

Léon, Sébastien, et al.. (2007). Characterization of Protein-Protein Interactions. Methods in molecular biology. 389. 219–237. 3 indexed citations

12.

Yan, Mingda, et al.. (2007). Dysferlin Domain-containing Proteins, Pex30p and Pex31p, Localized to Two Compartments, Control the Number and Size of Oleate-induced Peroxisomes in Pichia pastoris. Molecular Biology of the Cell. 19(3). 885–898. 69 indexed citations

13.

Tolstorukov, Ilya, Mingda Yan, Joan Lin‐Cereghino, et al.. (2006). Mxr1p, a Key Regulator of the Methanol Utilization Pathway and Peroxisomal Genes in Pichia pastoris. Molecular and Cellular Biology. 26(3). 883–897. 134 indexed citations

14.

Yan, Mingda, Naganand Rayapuram, & Suresh Subramani. (2005). The control of peroxisome number and size during division and proliferation. Current Opinion in Cell Biology. 17(4). 376–383. 106 indexed citations

15.

Xu, Weijing, Mingda Yan, Lanying Sun, Zhongcheng Zheng, & Xinyuan Liu. (2003). Ref‐1 protein enhances the IL‐2‐stimulated telomerase activity. Journal of Cellular Biochemistry. 88(6). 1120–1128. 3 indexed citations

16.

Xu, Weijing, Furong Yu, Mingda Yan, et al.. (2003). Geldanamycin, a heat shock protein 90‐binding agent, disrupts Stat5 activation in IL‐2‐stimulated cells. Journal of Cellular Physiology. 198(2). 188–196. 15 indexed citations

17.

Zou, Weiguo, Mingda Yan, Weijing Xu, et al.. (2001). Cobalt chloride induces PC12 cells apoptosis through reactive oxygen species and accompanied by AP‐1 activation. Journal of Neuroscience Research. 64(6). 646–653. 164 indexed citations

18.

Yan, Mingda, Weijin Xu, Linrong Lu, et al.. (2000). Induction of Ref-1 Ensures AP-1 Activation in Intracellular Oxidative Environment of IL-2-Stimulated BA/F3β Cells. Biochemical and Biophysical Research Communications. 278(2). 462–469. 7 indexed citations

19.

Lu, Linrong, Jinfang Zhu, Zhongcheng Zheng, et al.. (1998). Jak-STAT pathway is involved in the induction ofTNF-β gene during stimulation by IL-2. European Journal of Immunology. 28(3). 805–810. 32 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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