S Cheng

1.5k total citations
22 papers, 1.2k citations indexed

About

S Cheng is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, S Cheng has authored 22 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 5 papers in Cardiology and Cardiovascular Medicine and 4 papers in Surgery. Recurrent topics in S Cheng's work include Stock Market Forecasting Methods (3 papers), Lipoproteins and Cardiovascular Health (3 papers) and Paraoxonase enzyme and polymorphisms (3 papers). S Cheng is often cited by papers focused on Stock Market Forecasting Methods (3 papers), Lipoproteins and Cardiovascular Health (3 papers) and Paraoxonase enzyme and polymorphisms (3 papers). S Cheng collaborates with scholars based in United States, Switzerland and China. S Cheng's co-authors include Wayne M. Barnes, Russell Higuchi, Robert Y.L. Zee, Howard Gamper, Bennett Van Houten, Aziz Sancar, John E. Hearst, Michael Grow, Richard Reynolds and J. Hoh and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

S Cheng

21 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S Cheng United States 15 567 273 147 145 105 22 1.2k
Juan José Berlanga Spain 25 1.4k 2.4× 211 0.8× 254 1.7× 72 0.5× 187 1.8× 40 2.1k
Guido Sauer Germany 16 869 1.5× 172 0.6× 87 0.6× 89 0.6× 84 0.8× 21 1.5k
Ricardo Ramos Spain 20 700 1.2× 111 0.4× 108 0.7× 82 0.6× 247 2.4× 57 1.3k
Christian W. Heegaard Denmark 26 1.0k 1.8× 187 0.7× 313 2.1× 155 1.1× 61 0.6× 67 2.1k
Yonghong Zhu China 23 838 1.5× 204 0.7× 132 0.9× 75 0.5× 124 1.2× 43 1.5k
Changqing Zeng China 25 1.1k 1.9× 219 0.8× 127 0.9× 84 0.6× 61 0.6× 92 2.0k
John F. Peden United Kingdom 16 1.1k 2.0× 517 1.9× 238 1.6× 216 1.5× 150 1.4× 27 1.9k
James Fernandez United States 14 812 1.4× 81 0.3× 236 1.6× 76 0.5× 137 1.3× 24 1.3k
Rui Peng China 28 1.5k 2.7× 102 0.4× 353 2.4× 135 0.9× 127 1.2× 79 2.3k
Steven Ringquist United States 21 1.1k 1.9× 482 1.8× 74 0.5× 137 0.9× 121 1.2× 44 1.6k

Countries citing papers authored by S Cheng

Since Specialization
Citations

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

Fields of papers citing papers by S Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of S Cheng. A scholar is included among the top collaborators of S Cheng 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 S Cheng. S Cheng 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.
Zhang, Yue, et al.. (2024). An optimized LSTM network for improving arbitrage spread forecasting using ant colony cross-searching in the K-fold hyperparameter space. PeerJ Computer Science. 10. e2215–e2215. 1 indexed citations
4.
Claggett, Brian, et al.. (2019). Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis. Yearbook of pediatric endocrinology. 5 indexed citations
5.
Cheng, S & Ruth Murphy. (2011). Refractory aphthous ulceration treated with thalidomide: a report of 10 years’ clinical experience. Clinical and Experimental Dermatology. 37(2). 132–135. 6 indexed citations
6.
Zee, Robert Y.L., et al.. (2006). Multi‐locus candidate gene polymorphisms and risk of myocardial infarction: a population‐based, prospective genetic analysis. Journal of Thrombosis and Haemostasis. 4(2). 341–348. 42 indexed citations
7.
Krętowski, Adam, John E. Hokanson, Kim McFann, et al.. (2006). The apolipoprotein A-IV Gln360His polymorphism predicts progression of coronary artery calcification in patients with type 1 diabetes. Diabetologia. 49(8). 1946–1954. 26 indexed citations
8.
Baum, Larry, Emily Poon, Brian Tomlinson, et al.. (2006). Association of lipoprotein lipase S447X, apolipoprotein E exon 4, and apoC3 −455T>C polymorphisms on the susceptibility to diabetic nephropathy. Clinical Genetics. 70(1). 20–28. 33 indexed citations
9.
Burns, Jane C., Chisato Shimizu, Hiroko Shike, et al.. (2005). Family-based association analysis implicates IL-4 in susceptibility to Kawasaki disease. Genes and Immunity. 6(5). 438–444. 62 indexed citations
10.
Martinelli, Nicola, Domenico Girelli, Oliviero Olivieri, et al.. (2005). Interaction between metabolic syndrome and PON1 polymorphisms as a determinant of the risk of coronary artery disease. Clinical and Experimental Medicine. 5(1). 20–30. 20 indexed citations
11.
Penco, Silvana, Enzo Grossi, S Cheng, et al.. (2005). Assessment of the Role of Genetic Polymorphism in Venous Thrombosis Through Artificial Neural Networks. Annals of Human Genetics. 69(6). 693–706. 36 indexed citations
12.
Ip, Wai‐Ki, et al.. (2004). Serum Mannose‐Binding Lectin Levels and mbl2 Gene Polymorphisms in Different Age and Gender Groups of Southern Chinese Adults. Scandinavian Journal of Immunology. 59(3). 310–314. 45 indexed citations
13.
Martinelli, Nicola, Domenico Girelli, Oliviero Olivieri, et al.. (2004). Interaction between smoking and PON2 Ser311Cys polymorphism as a determinant of the risk of myocardial infarction. European Journal of Clinical Investigation. 34(1). 14–20. 45 indexed citations
14.
Zee, Robert Y.L., J. Hoh, S Cheng, et al.. (2002). Multi-locus interactions predict risk for post-PTCA restenosis: an approach to the genetic analysis of common complex disease. The Pharmacogenomics Journal. 2(3). 197–201. 53 indexed citations
15.
Hoh, J., Anja Wille, Robert Y.L. Zee, et al.. (2000). Selecting SNPs in two‐stage analysis of disease association data: a model‐free approach. Annals of Human Genetics. 64(5). 413–417. 78 indexed citations
16.
Pallaud, Céline, S Cheng, Michael Grow, et al.. (1999). Multilocus approach to cardiovascular risk. Scandinavian Journal of Clinical and Laboratory Investigation. 59(sup230). 168–176. 7 indexed citations
17.
Stewart, A C, Patti E. Gravitt, S Cheng, & Cosette M. Wheeler. (1995). Generation of entire human papillomavirus genomes by long PCR: frequency of errors produced during amplification.. Genome Research. 5(1). 79–88. 19 indexed citations
18.
Cheng, S, et al.. (1994). Effective amplification of long targets from cloned inserts and human genomic DNA.. Proceedings of the National Academy of Sciences. 91(12). 5695–5699. 496 indexed citations
19.
Pekarsky, Yuri, et al.. (1994). YAC clones targeting gene-rich regions of human Chromosome 3. Mammalian Genome. 5(6). 380–383. 3 indexed citations
20.
Cheng, S, Bennett Van Houten, Howard Gamper, Aziz Sancar, & John E. Hearst. (1988). Use of psoralen-modified oligonucleotides to trap three-stranded RecA-DNA complexes and repair of these cross-linked complexes by ABC excinuclease.. Journal of Biological Chemistry. 263(29). 15110–15117. 84 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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