Huiling He

5.8k total citations · 1 hit paper
52 papers, 3.3k citations indexed

About

Huiling He is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism and Cancer Research. According to data from OpenAlex, Huiling He has authored 52 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Molecular Biology, 20 papers in Endocrinology, Diabetes and Metabolism and 11 papers in Cancer Research. Recurrent topics in Huiling He's work include Thyroid Cancer Diagnosis and Treatment (20 papers), Cancer-related molecular mechanisms research (8 papers) and RNA modifications and cancer (8 papers). Huiling He is often cited by papers focused on Thyroid Cancer Diagnosis and Treatment (20 papers), Cancer-related molecular mechanisms research (8 papers) and RNA modifications and cancer (8 papers). Huiling He collaborates with scholars based in United States, China and Poland. Huiling He's co-authors include Albert de la Chapelle, Sandya Liyanarachchi, Rebecca Nagy, Clark Distelhorst, Saul Suster, Richard T. Kloos, Krystian Jażdżewski, Carlo M. Croce, George A. Calin and Stefano Volinia and has published in prestigious journals such as New England Journal of Medicine, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Huiling He

51 papers receiving 3.3k citations

Hit Papers

The role of microRNA genes in papillary thyroid carcinoma 2005 2026 2012 2019 2005 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Huiling He United States 24 2.2k 1.4k 818 443 397 52 3.3k
Elvira Haas Switzerland 22 1.2k 0.6× 818 0.6× 452 0.6× 602 1.4× 466 1.2× 35 2.8k
Giacomo Manenti Italy 30 1.8k 0.8× 590 0.4× 262 0.3× 663 1.5× 811 2.0× 102 3.0k
Marie C. Lin China 34 2.5k 1.2× 1.8k 1.3× 176 0.2× 146 0.3× 603 1.5× 56 3.7k
Alessandro Cama Italy 33 1.9k 0.9× 529 0.4× 690 0.8× 744 1.7× 748 1.9× 147 3.8k
Cecilia Garofalo Italy 26 1.1k 0.5× 581 0.4× 332 0.4× 279 0.6× 623 1.6× 41 2.5k
Ramendra K. Kundu United States 27 1.1k 0.5× 386 0.3× 421 0.5× 355 0.8× 180 0.5× 44 3.1k
Peter G. Zaphiropoulos Sweden 33 3.6k 1.7× 789 0.6× 440 0.5× 937 2.1× 867 2.2× 74 4.8k
Yasuhiro Mitsuuchi United States 24 1.9k 0.9× 230 0.2× 613 0.7× 305 0.7× 427 1.1× 37 2.9k
Michel Raymondjean France 31 2.2k 1.0× 371 0.3× 265 0.3× 639 1.4× 365 0.9× 70 3.1k
Inik Chang United States 27 1.9k 0.9× 1.4k 1.0× 95 0.1× 244 0.6× 281 0.7× 48 2.7k

Countries citing papers authored by Huiling He

Since Specialization
Citations

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

Fields of papers citing papers by Huiling He

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Huiling He

This figure shows the co-authorship network connecting the top 25 collaborators of Huiling He. A scholar is included among the top collaborators of Huiling He 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 Huiling He. Huiling He 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
3.
Comiskey, Daniel F., Huiling He, Sandya Liyanarachchi, et al.. (2020). Variants in LRRC34 reveal distinct mechanisms for predisposition to papillary thyroid carcinoma. Journal of Medical Genetics. 57(8). 519–527. 3 indexed citations
4.
He, Huiling, Wei Li, Daniel F. Comiskey, et al.. (2020). A Truncating Germline Mutation of TINF2 in Individuals with Thyroid Cancer or Melanoma Results in Longer Telomeres. Thyroid. 30(2). 204–213. 27 indexed citations
5.
Liyanarachchi, Sandya, Huiling He, Pamela Brock, et al.. (2019). Risk Haplotypes Uniquely Associated with Radioiodine-Refractory Thyroid Cancer Patients of High African Ancestry. Thyroid. 29(4). 530–539. 14 indexed citations
6.
Wang, Yanqiang, Sandya Liyanarachchi, Katherine E. Miller, et al.. (2019). Identification of Rare Variants Predisposing to Thyroid Cancer. Thyroid. 29(7). 946–955. 35 indexed citations
7.
Liyanarachchi, Sandya, Wei Li, Pearlly S. Yan, et al.. (2016). Genome-Wide Expression Screening Discloses Long Noncoding RNAs Involved in Thyroid Carcinogenesis. The Journal of Clinical Endocrinology & Metabolism. 101(11). 4005–4013. 48 indexed citations
8.
Tomšič, Jerneja, et al.. (2016). HABP2 G534E Variant in Papillary Thyroid Carcinoma. PLoS ONE. 11(1). e0146315–e0146315. 23 indexed citations
9.
Tomšič, Jerneja, Huiling He, & Albert de la Chapelle. (2015). HABP2 Mutation and Nonmedullary Thyroid Cancer.. New England Journal of Medicine. 373(21). 2086–2086. 13 indexed citations
10.
Tomšič, Jerneja, Huiling He, Keiko Akagi, et al.. (2015). A germline mutation in SRRM2, a splicing factor gene, is implicated in papillary thyroid carcinoma predisposition. Scientific Reports. 5(1). 10566–10566. 74 indexed citations
11.
He, Huiling, Roger L. Nation, Jovan Jacob, et al.. (2013). Pharmacokinetics of four different brands of colistimethate and formed colistin in rats. Journal of Antimicrobial Chemotherapy. 68(10). 2311–7. 68 indexed citations
12.
Liyanarachchi, Sandya, Anna Wójcicka, Małgorzata Czetwertyńska, et al.. (2013). Cumulative Risk Impact of Five Genetic Variants Associated with Papillary Thyroid Carcinoma. Thyroid. 23(12). 1532–1540. 56 indexed citations
13.
Jendrzejewski, Jarosław, Huiling He, Hanna S. Radomska, et al.. (2012). The polymorphism rs944289 predisposes to papillary thyroid carcinoma through a large intergenic noncoding RNA gene of tumor suppressor type. Proceedings of the National Academy of Sciences. 109(22). 8646–8651. 207 indexed citations
14.
Iuliano, Rodolfo, Dario Palmieri, Huiling He, et al.. (2010). Role of PTPRJ genotype in papillary thyroid carcinoma risk. Endocrine Related Cancer. 17(4). 1001–1006. 18 indexed citations
15.
He, Huiling, Rebecca Nagy, Sandya Liyanarachchi, et al.. (2009). A Susceptibility Locus for Papillary Thyroid Carcinoma on Chromosome 8q24. Cancer Research. 69(2). 625–631. 115 indexed citations
16.
Vasko, Vasily, Allan V. Espinosa, Huiling He, et al.. (2007). Gene expression and functional evidence of epithelial-to-mesenchymal transition in papillary thyroid carcinoma invasion. Proceedings of the National Academy of Sciences. 104(8). 2803–2808. 270 indexed citations
17.
He, Huiling, et al.. (2005). Recognition and binding of mismatch repair proteins at an oncogenic hot spot. BMC Molecular Biology. 6(1). 6–6. 8 indexed citations
18.
He, Huiling, Karen McColl, & Clark Distelhorst. (2000). Involvement of c-Fos in signaling grp78 induction following ER calcium release. Oncogene. 19(51). 5936–5943. 20 indexed citations
19.
McColl, Karen, Huiling He, Hongying Zhong, et al.. (1998). Apoptosis induction by the glucocorticoid hormone dexamethasone and the calcium-ATPase inhibitor thapsigargin involves Bc1-2 regulated caspase activation. Molecular and Cellular Endocrinology. 139(1-2). 229–238. 59 indexed citations
20.
He, Huiling, et al.. (1998). c-Fos Degradation by the Proteasome. Journal of Biological Chemistry. 273(39). 25015–25019. 59 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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