Fu Liang Ng

2.5k total citations
25 papers, 693 citations indexed

About

Fu Liang Ng is a scholar working on Cardiology and Cardiovascular Medicine, Genetics and Pharmacology. According to data from OpenAlex, Fu Liang Ng has authored 25 papers receiving a total of 693 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Cardiology and Cardiovascular Medicine, 7 papers in Genetics and 5 papers in Pharmacology. Recurrent topics in Fu Liang Ng's work include Genetic Associations and Epidemiology (5 papers), Pharmacogenetics and Drug Metabolism (5 papers) and Hormonal Regulation and Hypertension (4 papers). Fu Liang Ng is often cited by papers focused on Genetic Associations and Epidemiology (5 papers), Pharmacogenetics and Drug Metabolism (5 papers) and Hormonal Regulation and Hypertension (4 papers). Fu Liang Ng collaborates with scholars based in United Kingdom, China and United States. Fu Liang Ng's co-authors include Mark J. Caulfield, Shu Ye, Qingzhong Xiao, Kenneth Chan, Xiangyuan Pu, Robin N. Poston, Arthur Tucker, Helen R. Warren, Ruoxin Zhang and Iain MacPhee and has published in prestigious journals such as Hypertension, The American Journal of Human Genetics and Human Molecular Genetics.

In The Last Decade

Fu Liang Ng

24 papers receiving 683 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fu Liang Ng United Kingdom 14 295 212 137 123 77 25 693
Ganesh Chauhan India 16 240 0.8× 76 0.4× 275 2.0× 76 0.6× 106 1.4× 28 769
Lydia Coulter Kwee United States 17 410 1.4× 120 0.6× 244 1.8× 68 0.6× 74 1.0× 45 990
Andrea Baessler Germany 20 330 1.1× 345 1.6× 146 1.1× 46 0.4× 187 2.4× 53 1.1k
Teresa Tusié‐Luna Mexico 17 343 1.2× 199 0.9× 257 1.9× 104 0.8× 213 2.8× 34 888
David Seo United States 17 361 1.2× 160 0.8× 127 0.9× 119 1.0× 139 1.8× 41 885
Sandra Romero‐Hidalgo Mexico 16 223 0.8× 86 0.4× 181 1.3× 53 0.4× 171 2.2× 36 813
Nasser A. Dhayat Switzerland 17 425 1.4× 407 1.9× 106 0.8× 63 0.5× 153 2.0× 43 1.2k
Delyth Graham United Kingdom 18 232 0.8× 293 1.4× 51 0.4× 58 0.5× 75 1.0× 32 839
Jayashree Shanker United Kingdom 17 169 0.6× 109 0.5× 137 1.0× 58 0.5× 109 1.4× 38 604
Miriam Dreßler Germany 15 207 0.7× 136 0.6× 61 0.4× 45 0.4× 142 1.8× 28 790

Countries citing papers authored by Fu Liang Ng

Since Specialization
Citations

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

Fields of papers citing papers by Fu Liang Ng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fu Liang Ng. 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 Fu Liang Ng. The network helps show where Fu Liang Ng may publish in the future.

Co-authorship network of co-authors of Fu Liang Ng

This figure shows the co-authorship network connecting the top 25 collaborators of Fu Liang Ng. A scholar is included among the top collaborators of Fu Liang Ng 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 Fu Liang Ng. Fu Liang Ng 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.
Shah, Rachna, et al.. (2025). Drugs affecting the autonomic nervous system. Anaesthesia & intensive care medicine. 26(7). 446–451.
2.
Magavern, Emma, Deepti Gurdasani, Fu Liang Ng, & Sandra Soo‐Jin Lee. (2021). Health equality, race and pharmacogenomics. British Journal of Clinical Pharmacology. 88(1). 27–33. 36 indexed citations
3.
Maniero, Carmela, et al.. (2021). A retrospective cohort study of risk factors and outcomes in older patients admitted to an inner-city geriatric unit in London during first peak of COVID-19 pandemic. Irish Journal of Medical Science (1971 -). 191(3). 1037–1045. 9 indexed citations
4.
5.
Cabrera, Claudia, Fu Liang Ng, Ajay Gupta, et al.. (2019). Over 1000 genetic loci influencing blood pressure with multiple systems and tissues implicated. Human Molecular Genetics. 28(R2). R151–R161. 39 indexed citations
6.
Ng, Fu Liang, Karilynn Rockhill, Joshua C. Black, et al.. (2018). UK survey of non-medical use of prescription drugs (NMURx) as a valuable source of general population illicit drug use data. Postgraduate Medical Journal. 94(1117). 627–633. 7 indexed citations
7.
Ng, Fu Liang, Helen R. Warren, & Mark J. Caulfield. (2018). Hypertension genomics and cardiovascular prevention. Annals of Translational Medicine. 6(15). 291–291. 22 indexed citations
8.
Ren, Meixia, Fu Liang Ng, Helen R. Warren, et al.. (2017). The biological impact of blood pressure-associated genetic variants in the natriuretic peptide receptor C gene on human vascular smooth muscle. Human Molecular Genetics. 27(1). 199–210. 26 indexed citations
9.
Ng, Fu Liang, Ebbe Boedtkjer, Katarzyna Witkowska, et al.. (2017). Increased NBCn1 expression, Na + /HCO 3 - co-transport and intracellular pH in human vascular smooth muscle cells with a risk allele for hypertension. Human Molecular Genetics. 26(5). ddx015–ddx015. 21 indexed citations
10.
Ng, Fu Liang, Ebbe Boedtkjer, Shu Ye, & Mark J. Caulfield. (2016). LBOS 02-04 BLOOD PRESSURE-ASSOCIATED POLYMORPHISMS IN SLC4A7 (SODIUM/BICARBONATE CO-TRANSPORTER NBCn1) ARE LINKED TO GENE EXPRESSION AND INTRACELLULAR pH REGULATION. Journal of Hypertension. 34(Supplement 1). e549–e550. 1 indexed citations
11.
Zhang, Ruoxin, Kate Witkowska, José Afonso Guerra‐Assunção, et al.. (2016). A blood pressure-associated variant of theSLC39A8gene influences cellular cadmium accumulation and toxicity. Human Molecular Genetics. 25(18). 4117–4126. 43 indexed citations
12.
13.
Ng, Fu Liang, Manish Saxena, Felix Mahfoud, Atul Pathak, & Melvin D. Lobo. (2016). Device-based Therapy for Hypertension. Current Hypertension Reports. 18(8). 61–61. 28 indexed citations
14.
Witkowska, Kate, et al.. (2015). LB03.08. Journal of Hypertension. 33(Supplement 1). e128–e128. 2 indexed citations
15.
Ng, Fu Liang, et al.. (2013). e‐ L earning to facilitate preparation for prescribing skills assessment. Medical Education. 47(5). 520–520. 1 indexed citations
16.
Pu, Xiangyuan, Qingzhong Xiao, Stefan Kiechl, et al.. (2013). ADAMTS7 Cleavage and Vascular Smooth Muscle Cell Migration Is Affected by a Coronary-Artery-Disease-Associated Variant. The American Journal of Human Genetics. 92(3). 366–374. 81 indexed citations
17.
Motterle, Anna, Xiangyuan Pu, Henry M. Wood, et al.. (2012). Functional analyses of coronary artery disease associated variation on chromosome 9p21 in vascular smooth muscle cells. Human Molecular Genetics. 21(18). 4021–4029. 133 indexed citations
18.
Ng, Fu Liang, Alison Davis, Thomas A. Jepps, et al.. (2010). Expression and function of the K+channelKCNQgenes in human arteries. British Journal of Pharmacology. 162(1). 42–53. 107 indexed citations
19.
Ng, Fu Liang, David W. Holt, Rin Chang, & Iain MacPhee. (2009). Black renal transplant recipients have poorer long-term graft survival than CYP3A5 expressers from other ethnic groups. Nephrology Dialysis Transplantation. 25(2). 628–634. 23 indexed citations
20.
Ng, Fu Liang, David W. Holt, & Iain MacPhee. (2007). Pharmacogenetics as a tool for optimising drug therapy in solid-organ transplantation. Expert Opinion on Pharmacotherapy. 8(13). 2045–2058. 18 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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