Lan-ying Gou

703 total citations
11 papers, 433 citations indexed

About

Lan-ying Gou is a scholar working on Pulmonary and Respiratory Medicine, Oncology and Molecular Biology. According to data from OpenAlex, Lan-ying Gou has authored 11 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Pulmonary and Respiratory Medicine, 8 papers in Oncology and 4 papers in Molecular Biology. Recurrent topics in Lan-ying Gou's work include Lung Cancer Treatments and Mutations (8 papers), Lung Cancer Research Studies (3 papers) and RNA modifications and cancer (2 papers). Lan-ying Gou is often cited by papers focused on Lung Cancer Treatments and Mutations (8 papers), Lung Cancer Research Studies (3 papers) and RNA modifications and cancer (2 papers). Lan-ying Gou collaborates with scholars based in China. Lan-ying Gou's co-authors include Yi‐Long Wu, Wen‐Zhao Zhong, Xu‐Chao Zhang, Jian Su, Zhi Xie, Jin‐Ji Yang, Zhong‐Yi Dong, Feiyu Niu, Jian Zhang and Lifang Cheng and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Cancer.

In The Last Decade

Lan-ying Gou

11 papers receiving 430 citations

Peers

Lan-ying Gou

ONCOL

PRM

MB

CR

IMMUN

Rico D. Bense Netherlands

Sinéad Noonan Ireland

M. D’Arcangelo Italy

Peter Schirmacher Germany

Danmei Pang China

Davide Bosso Italy

Liam Cornell United States

Maria Saigí Spain

Mark D. Kochenderfer United States

Chester Kao United States

Rico D. Bense Netherlands

Lan-ying Gou

298 ×1.2

ONCOL

166 ×0.7

PRM

177 ×1.2

MB

141 ×1.5

CR

153 ×1.8

IMMUN

Citations per year, relative to Lan-ying Gou Lan-ying Gou (= 1×) peers Rico D. Bense

Countries citing papers authored by Lan-ying Gou

Since Specialization

Citations

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

Fields of papers citing papers by Lan-ying Gou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lan-ying Gou. 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 Lan-ying Gou. The network helps show where Lan-ying Gou may publish in the future.

Co-authorship network of co-authors of Lan-ying Gou

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

All Works

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11 of 11 papers shown

1.

Zhang, Nan, Jie Shen, Lan-ying Gou, et al.. (2022). UBE3A deletion enhances the efficiency of immunotherapy in non-small-cell lung cancer. Bioengineered. 13(5). 11577–11592. 8 indexed citations

2.

Cheng, Lifang, Lan-ying Gou, Ting Wei, & Jian Zhang. (2020). GBP1 promotes erlotinib resistance via PGK1‑activated EMT signaling in non‑small cell lung cancer. International Journal of Oncology. 57(3). 858–870. 30 indexed citations

3.

Li, Man, Anqi Lin, Peng Luo, et al.. (2020). DNAH10 mutation correlates with cisplatin sensitivity and tumor mutation burden in small-cell lung cancer. Aging. 12(2). 1285–1303. 25 indexed citations

4.

Wu, Si-Pei, Ri-Qiang Liao, Hai‐Yan Tu, et al.. (2017). Stromal PD-L1–Positive Regulatory T cells and PD-1–Positive CD8-Positive T cells Define the Response of Different Subsets of Non–Small Cell Lung Cancer to PD-1/PD-L1 Blockade Immunotherapy. Journal of Thoracic Oncology. 13(4). 521–532. 120 indexed citations

5.

Zhang, Xu‐Chao, Shejuan An, Zhihong Chen, et al.. (2016). Blockade of Hedgehog Signaling Synergistically Increases Sensitivity to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Non-Small-Cell Lung Cancer Cell Lines. PLoS ONE. 11(3). e0149370–e0149370. 63 indexed citations

6.

Liu, Si‐Yang Maggie, Lan-ying Gou, Anna Li, et al.. (2016). The Unique Characteristics of MET Exon 14 Mutation in Chinese Patients with NSCLC. Journal of Thoracic Oncology. 11(9). 1503–1510. 55 indexed citations

7.

Gou, Lan-ying, Anna Li, Jin‐Ji Yang, et al.. (2016). The coexistence of MET over-expression and an EGFR T790M mutation is related to acquired resistance to EGFR tyrosine kinase inhibitors in advanced non-small cell lung cancer. Oncotarget. 7(32). 51311–51319. 26 indexed citations

8.

Yang, Lulu, Xu‐Chao Zhang, Shaokun Chuai, et al.. (2015). Integrative Analyses of Lung Squamous Cell Carcinoma in Ten Chinese Patients with Transcriptome Sequencing. Journal of genetics and genomics. 42(10). 579–587. 2 indexed citations

9.

Gou, Lan-ying, Yi‐Long Wu, Jin‐Ji Yang, & Xu‐Chao Zhang. (2015). Targeting c-Met overexpression for overcoming acquired resistance to EGFR TKIs in NSCLC.. Journal of Clinical Oncology. 33(15_suppl). 8091–8091. 2 indexed citations

10.

Gou, Lan-ying, Feiyu Niu, Yi‐Long Wu, & Wen‐Zhao Zhong. (2015). Differences in driver genes between smoking‐related and non–smoking‐related lung cancer in the Chinese population. Cancer. 121(S17). 3069–3079. 51 indexed citations

11.

Wu, Yi‐Long & Lan-ying Gou. (2014). Prevalence of driver mutations in non-small-cell lung cancers in the People’s Republic of China. PubMed. 5. 1–1. 51 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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