Shaolin Liang

1.6k total citations · 1 hit paper
15 papers, 1.2k citations indexed

About

Shaolin Liang is a scholar working on Molecular Biology, Biomedical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Shaolin Liang has authored 15 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 5 papers in Biomedical Engineering and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Shaolin Liang's work include Advanced biosensing and bioanalysis techniques (10 papers), Biosensors and Analytical Detection (4 papers) and Molecular Junctions and Nanostructures (3 papers). Shaolin Liang is often cited by papers focused on Advanced biosensing and bioanalysis techniques (10 papers), Biosensors and Analytical Detection (4 papers) and Molecular Junctions and Nanostructures (3 papers). Shaolin Liang collaborates with scholars based in Hong Kong, China and United Kingdom. Shaolin Liang's co-authors include Roland Somogyi, Patrik D’haeseleer, Julian A. Tanner, Andrew B. Kinghorn, Roderick M. Dirkzwager, Lewis A. Fraser, Simon Chi‐Chin Shiu, Yee‐Wai Cheung, Jeffrey J. Seilhamer and Mary Jane Cunningham and has published in prestigious journals such as Nature Communications, Bioinformatics and Analytical Chemistry.

In The Last Decade

Shaolin Liang

14 papers receiving 1.1k citations

Hit Papers

align trajectories

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.

Genetic network inference: from co-expression clustering to reverse engineering

2000 643 citations Patrik D’haeseleer, Shaolin Liang et al. Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shaolin Liang Hong Kong	10	990	239	87	75	62	15	1.2k
Pakpoom Subsoontorn Thailand	9	684 0.7×	177 0.7×	38 0.4×	129 1.7×	45 0.7×	14	872
Dirk Labudde Germany	18	761 0.8×	60 0.3×	36 0.4×	103 1.4×	30 0.5×	80	1.1k
Marko Storch United Kingdom	17	901 0.9×	258 1.1×	18 0.2×	142 1.9×	32 0.5×	33	1.3k
Macdonald Morris United States	7	1.0k 1.0×	216 0.9×	37 0.4×	215 2.9×	89 1.4×	7	1.4k
Christoffer Norn United States	13	878 0.9×	96 0.4×	22 0.3×	52 0.7×	20 0.3×	18	1.1k
Liliana Wróblewska United States	16	1.4k 1.4×	169 0.7×	17 0.2×	241 3.2×	41 0.7×	18	1.6k
Kyu‐Ho Park South Korea	16	424 0.4×	74 0.3×	39 0.4×	43 0.6×	131 2.1×	56	920
Kui Xu China	16	768 0.8×	57 0.2×	24 0.3×	128 1.7×	31 0.5×	38	1.2k
Jeff Nivala United States	15	1.1k 1.2×	604 2.5×	96 1.1×	90 1.2×	165 2.7×	26	1.5k

Countries citing papers authored by Shaolin Liang

Since Specialization

Citations

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

Fields of papers citing papers by Shaolin Liang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shaolin Liang

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Liang, Shaolin, et al.. (2025). Emergency supplier evaluation framework fusing probabilistic hesitant fuzzy set group decision-making and clustering-based methods. Journal of Intelligent & Fuzzy Systems. 48(4). 491–505. 1 indexed citations

Liang, Shaolin, et al.. (2024). A wireless electrochemical Aptamer-Based biosensor platform utilizing printed circuit board electrodes for drug monitoring applications. Microchemical Journal. 204. 111038–111038. 6 indexed citations

Liang, Shaolin, et al.. (2024). A Deep Graph Convolution Network-Based Abnormity Detection Model for Largescale Behavioral Data. IEEE Access. 12. 94380–94392.

Liang, Shaolin, et al.. (2023). A Fast Scenario Transfer Approach for Portrait Styles Through Collaborative Awareness of Convolutional Neural Network and Generative Adversarial Learning. Journal of Circuits Systems and Computers. 33(7). 2 indexed citations

Lo, Young, Shaolin Liang, Waljit S. Dhillo, Anthony E. G. Cass, & Julian A. Tanner. (2022). Robotic APTamer-Enabled Electrochemical Reader (RAPTER) System for Automated Aptamer-Mediated Electrochemical Analysis. Methods in molecular biology. 2570. 271–280. 2 indexed citations

Liang, Shaolin, Andrew B. Kinghorn, Margaritis Voliotis, et al.. (2019). Measuring luteinising hormone pulsatility with a robotic aptamer-enabled electrochemical reader. Nature Communications. 10(1). 852–852. 64 indexed citations

Shiu, Simon Chi‐Chin, Yee‐Wai Cheung, Shaolin Liang, et al.. (2018). An aptamer-enabled DNA nanobox for protein sensing. Nanomedicine Nanotechnology Biology and Medicine. 14(4). 1161–1168. 44 indexed citations

Fraser, Lewis A., Andrew B. Kinghorn, Roderick M. Dirkzwager, et al.. (2017). A portable microfluidic Aptamer-Tethered Enzyme Capture (APTEC) biosensor for malaria diagnosis. Biosensors and Bioelectronics. 100. 591–596. 84 indexed citations

Kinghorn, Andrew B., Lewis A. Fraser, Shaolin Liang, Simon Chi‐Chin Shiu, & Julian A. Tanner. (2017). Aptamer Bioinformatics. International Journal of Molecular Sciences. 18(12). 2516–2516. 121 indexed citations

10.

Kinghorn, Andrew B., Roderick M. Dirkzwager, Shaolin Liang, et al.. (2016). Aptamer Affinity Maturation by Resampling and Microarray Selection. Analytical Chemistry. 88(14). 6981–6985. 30 indexed citations

11.

Dirkzwager, Roderick M., Shaolin Liang, & Julian A. Tanner. (2016). Development of Aptamer-Based Point-of-Care Diagnostic Devices for Malaria Using Three-Dimensional Printing Rapid Prototyping. ACS Sensors. 1(4). 420–426. 77 indexed citations

12.

Shiu, Simon Chi‐Chin, Yee‐Wai Cheung, Roderick M. Dirkzwager, et al.. (2016). Aptamer‐Mediated Protein Molecular Recognition Driving a DNA Tweezer Nanomachine. Advanced Biosystems. 1(1-2). e1600006–e1600006. 29 indexed citations

13.

Fraser, Lewis A., Andrew B. Kinghorn, Yee‐Wai Cheung, et al.. (2015). Oligonucleotide Functionalised Microbeads: Indispensable Tools for High-Throughput Aptamer Selection. Molecules. 20(12). 21298–21312. 15 indexed citations

14.

D’haeseleer, Patrik, Shaolin Liang, & Roland Somogyi. (2000). Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics. 16(8). 707–726. 643 indexed citations breakdown →

15.

Cunningham, Mary Jane, et al.. (2000). Gene Expression Microarray Data Analysis for Toxicology Profiling. Annals of the New York Academy of Sciences. 919(1). 52–67. 38 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.

Explore authors with similar magnitude of impact