Wen‐Ling Chan

1.9k total citations · 1 hit paper
27 papers, 1.4k citations indexed

About

Wen‐Ling Chan is a scholar working on Molecular Biology, Cancer Research and Epidemiology. According to data from OpenAlex, Wen‐Ling Chan has authored 27 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 9 papers in Cancer Research and 5 papers in Epidemiology. Recurrent topics in Wen‐Ling Chan's work include RNA modifications and cancer (6 papers), Cancer-related molecular mechanisms research (5 papers) and Hepatocellular Carcinoma Treatment and Prognosis (4 papers). Wen‐Ling Chan is often cited by papers focused on RNA modifications and cancer (6 papers), Cancer-related molecular mechanisms research (5 papers) and Hepatocellular Carcinoma Treatment and Prognosis (4 papers). Wen‐Ling Chan collaborates with scholars based in Taiwan, Japan and India. Wen‐Ling Chan's co-authors include Hsien‐Da Huang, Weiyun Wu, Feng‐Mao Lin, Chi‐Ying F. Huang, Ann‐Ping Tsou, Chih-Min Chiu, Chia-Hung Chien, Chao Liang, Wei‐Chih Huang and Sheng‐Da Hsu and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and International Journal of Molecular Sciences.

In The Last Decade

Wen‐Ling Chan

26 papers receiving 1.4k citations

Hit Papers

miRTarBase: a database curates experimentally validated m... 2010 2026 2015 2020 2010 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. miRTarBase: a database curates experimentally validated microRNA–target interactions
    2010 1.1k citations Sheng‐Da Hsu, Feng‐Mao Lin et al. Nucleic Acids Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wen‐Ling Chan Taiwan 10 1.1k 908 86 86 81 27 1.4k
Xiaomei Li China 19 414 0.4× 268 0.3× 48 0.6× 94 1.1× 42 0.5× 58 844
Francesca Tavano Italy 16 381 0.4× 275 0.3× 130 1.5× 43 0.5× 32 0.4× 38 732
Rui Xu China 20 659 0.6× 385 0.4× 21 0.2× 200 2.3× 54 0.7× 59 1.1k
Henriett Butz Hungary 22 1.1k 1.0× 937 1.0× 25 0.3× 88 1.0× 146 1.8× 94 1.8k
Jean-Louis Danan France 22 559 0.5× 169 0.2× 65 0.8× 93 1.1× 62 0.8× 41 1.0k
Euan J. Rodger New Zealand 19 1.1k 1.0× 343 0.4× 17 0.2× 186 2.2× 67 0.8× 56 1.4k
Yingcheng Wu China 15 523 0.5× 314 0.3× 59 0.7× 195 2.3× 54 0.7× 32 987
John P. Thomson United Kingdom 21 1.4k 1.3× 227 0.3× 19 0.2× 117 1.4× 222 2.7× 37 2.0k
Christer Ericsson Sweden 18 600 0.6× 233 0.3× 13 0.2× 180 2.1× 113 1.4× 40 1.3k
Eunjoo Choi South Korea 14 543 0.5× 392 0.4× 10 0.1× 40 0.5× 38 0.5× 20 845

Countries citing papers authored by Wen‐Ling Chan

Since Specialization
Citations

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

Fields of papers citing papers by Wen‐Ling Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wen‐Ling Chan. 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 Wen‐Ling Chan. The network helps show where Wen‐Ling Chan may publish in the future.

Co-authorship network of co-authors of Wen‐Ling Chan

This figure shows the co-authorship network connecting the top 25 collaborators of Wen‐Ling Chan. A scholar is included among the top collaborators of Wen‐Ling Chan 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 Wen‐Ling Chan. Wen‐Ling Chan 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
1.
Chan, Wen‐Ling, et al.. (2025). Independent prognostic significance of tissue and circulating microrna biomarkers in hepatocellular carcinoma. Discover Oncology. 16(1). 281–281.
2.
Jeng, Long‐Bin, et al.. (2025). Cytokine biomarkers for independent prediction of hepatocellular carcinoma prognosis. Discover Oncology. 16(1). 421–421. 1 indexed citations
3.
Jeng, Long‐Bin, Wen‐Ling Chan, & Chiao‐Fang Teng. (2025). Molecular Mechanisms and Therapeutic Targets of Hepatitis B Virus Pre-S Mutant-Associated Hepatocellular Carcinoma Tumorigenesis. Cancer Control. 32. 2935738540–2935738540. 1 indexed citations
4.
Jeng, Long‐Bin, Wen‐Ling Chan, & Chiao‐Fang Teng. (2023). Prognostic Significance of Serum Albumin Level and Albumin-Based Mono- and Combination Biomarkers in Patients with Hepatocellular Carcinoma. Cancers. 15(4). 1005–1005. 20 indexed citations
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Teng, Chiao‐Fang, Tsai‐Chung Li, Hsi‐Yuan Huang, et al.. (2020). Hepatitis B virus pre-S2 deletion (nucleotide 1 to 54) in plasma predicts recurrence of hepatocellular carcinoma after curative surgical resection. PLoS ONE. 15(11). e0242748–e0242748. 10 indexed citations
9.
Chang, Chun‐Chi, Ting‐Yuan Liu, Yu‐Chia Chen, et al.. (2018). Genome-wide analysis of lncRNAs in 3'-untranslated regions: CR933609 acts as a decoy to protect the INO80D gene. International Journal of Oncology. 53(1). 417–433. 9 indexed citations
10.
11.
Chan, Wen‐Ling & Jan‐Gowth Chang. (2014). Pseudogene-Derived Endogenous siRNAs and Their Function. Methods in molecular biology. 1167. 227–239. 20 indexed citations
12.
Chan, Wen‐Ling, Hsien‐Da Huang, & Jan‐Gowth Chang. (2014). lncRNAMap: A map of putative regulatory functions in the long non-coding transcriptome. Computational Biology and Chemistry. 50. 41–49. 33 indexed citations
13.
Hsu, Sheng‐Da, Feng‐Mao Lin, Weiyun Wu, et al.. (2010). miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic Acids Research. 39(suppl_1). D163–D169. 1074 indexed citations breakdown →
14.
Shih, Mu‐Chin, Konan Peck, Wen‐Ling Chan, et al.. (2005). SARS-CoV Infection Was from at Least Two Origins in the Taiwan Area. Intervirology. 48(2-3). 124–132. 2 indexed citations
15.
Yeh, Kun‐Tu, Ming‐Yu Yang, Ta‐Chih Liu, et al.. (2005). Abnormal expression of period 1 (PER1) in endometrial carcinoma. The Journal of Pathology. 206(1). 111–120. 106 indexed citations
16.
Chang, Jan‐Gowth, et al.. (2002). Hb G-CHINESE: A G → C SUBSTITUTION AT CODON 30 OF THE α2-GLOBIN GENE CREATES APstI CUTTING SITE. Hemoglobin. 26(1). 95–97. 4 indexed citations
17.
Chang, Jan‐Gowth, Mu‐Chin Shih, Chih‐Mei Chen, et al.. (2002). Hb G-HONOLULU [α30(B11)Glu→Gln (α2)], Hb J-MEINUNG [β56(D7)Gly→Asp], ANDβ-THALASSEMIA [CODONS 41/42 (—TCTT)] IN A TAIWANESE FAMILY. Hemoglobin. 26(3). 325–328. 5 indexed citations
18.
Chen, Chih‐Mei, et al.. (2002). Hb UBE-2 IN A TAIWANESE SUBJECT: AN A → G SUBSTITUTION AT CODON 68 OF THE α2-GLOBIN GENE. Hemoglobin. 26(1). 99–101. 1 indexed citations
19.
Chang, Jan‐Gowth, et al.. (2001). Hb MANITOBA IN A TAIWANESE FAMILY: A C → A SUBSTITUTION AT CODON 102 OF THE α2-GLOBIN GENE. Hemoglobin. 25(4). 437–439. 7 indexed citations
20.
Laue, Louisa, Wen‐Ling Chan, & Su Mei Wu. (1994). Genetic heterogeneity of activating mutations of the luteinizing hormone receptor gene in familial male-limited precocious puberty. The American Journal of Human Genetics. 55. 5 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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