Suryani Lukman

888 total citations
25 papers, 710 citations indexed

About

Suryani Lukman is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cell Biology. According to data from OpenAlex, Suryani Lukman has authored 25 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 4 papers in Computational Theory and Mathematics and 4 papers in Cell Biology. Recurrent topics in Suryani Lukman's work include Protein Structure and Dynamics (6 papers), Computational Drug Discovery Methods (4 papers) and Alzheimer's disease research and treatments (3 papers). Suryani Lukman is often cited by papers focused on Protein Structure and Dynamics (6 papers), Computational Drug Discovery Methods (4 papers) and Alzheimer's disease research and treatments (3 papers). Suryani Lukman collaborates with scholars based in Singapore, United Arab Emirates and United Kingdom. Suryani Lukman's co-authors include Yulan He, J. Andrew McCammon, Barry J. Grant, Alemayehu A. Gorfe, Chandra Verma, Guy H. Grant, Sungmun Lee, David P. Lane, Joan Heller Brown and Harrison J. Hocker and has published in prestigious journals such as PLoS ONE, Scientific Reports and FEBS Letters.

In The Last Decade

Suryani Lukman

25 papers receiving 695 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suryani Lukman Singapore 13 496 101 98 79 78 25 710
Kana Shimizu Japan 19 772 1.6× 21 0.2× 117 1.2× 46 0.6× 63 0.8× 70 1.2k
Xujun Liang China 17 705 1.4× 140 1.4× 25 0.3× 29 0.4× 231 3.0× 36 1.2k
Ni Ai China 20 621 1.3× 147 1.5× 26 0.3× 35 0.4× 129 1.7× 33 1.0k
K. Srinivas India 10 428 0.9× 14 0.1× 24 0.2× 50 0.6× 101 1.3× 26 793
Xuejiao Cui China 10 1.0k 2.1× 69 0.7× 36 0.4× 20 0.3× 349 4.5× 14 1.5k
Zhining Wen China 17 1.2k 2.4× 39 0.4× 38 0.4× 18 0.2× 297 3.8× 70 1.5k
Xiaoxu Li China 7 707 1.4× 91 0.9× 33 0.3× 22 0.3× 329 4.2× 17 1.1k
Rong Chen China 19 849 1.7× 22 0.2× 151 1.5× 47 0.6× 113 1.4× 68 1.2k
Jenny Forshed Sweden 20 838 1.7× 34 0.3× 14 0.1× 31 0.4× 22 0.3× 32 1.2k
Junbo Gao China 16 622 1.3× 24 0.2× 81 0.8× 10 0.1× 298 3.8× 47 926

Countries citing papers authored by Suryani Lukman

Since Specialization
Citations

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

Fields of papers citing papers by Suryani Lukman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suryani Lukman

This figure shows the co-authorship network connecting the top 25 collaborators of Suryani Lukman. A scholar is included among the top collaborators of Suryani Lukman 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 Suryani Lukman. Suryani Lukman 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.
Lee, Sungmun, et al.. (2020). Aggregation and Cellular Toxicity of Pathogenic or Non-pathogenic Proteins. Scientific Reports. 10(1). 5120–5120. 46 indexed citations
2.
Lukman, Suryani, et al.. (2020). Inhibition of lysozyme aggregation and cellular toxicity by organic acids at acidic and physiological pH conditions. International Journal of Biological Macromolecules. 149. 921–930. 23 indexed citations
3.
Lukman, Suryani, et al.. (2018). Allosteric binding sites in Rab11 for potential drug candidates. PLoS ONE. 13(6). e0198632–e0198632. 12 indexed citations
4.
Choi, Myung Chul, et al.. (2018). Inhibition of Human Amylin Aggregation and Cellular Toxicity by Lipoic Acid and Ascorbic Acid. Molecular Pharmaceutics. 15(6). 2098–2106. 42 indexed citations
5.
Svetinović, Davor, et al.. (2017). Malware detection in android mobile platform using machine learning algorithms. 763–768. 21 indexed citations
6.
Lukman, Suryani, Minh N. Nguyen, Kelvin Sim, & Jeremy Teo. (2017). Discovery of Rab1 binding sites using an ensemble of clustering methods. Proteins Structure Function and Bioinformatics. 85(5). 859–871. 6 indexed citations
7.
Lukman, Suryani. (2016). Novel Druggable Sites of Insulin-Degrading Enzyme Identified through Applied Structural Bioinformatics Analysis. Procedia Computer Science. 80. 2292–2296. 5 indexed citations
8.
Lukman, Suryani, Habiba Al Safar, Sungmun Lee, & Kelvin Sim. (2015). Harnessing Structural Data of Insulin and Insulin Receptor for Therapeutic Designs. Journal of Endocrinology and Metabolism. 5(5). 273–283. 8 indexed citations
9.
10.
Roemer, E., Ruth Dempsey, Suryani Lukman, et al.. (2014). Toxicological assessment of kretek cigarettes part 4: Mechanistic investigations, smoke chemistry and in vitro toxicity. Regulatory Toxicology and Pharmacology. 70. S41–S53. 8 indexed citations
11.
Lukman, Suryani, Chandra Verma, & Gloria Fuentes. (2013). Exploiting Protein Intrinsic Flexibility in Drug Design. Advances in experimental medicine and biology. 805. 245–269. 7 indexed citations
12.
Lukman, Suryani, David P. Lane, & Chandra Verma. (2013). Mapping the Structural and Dynamical Features of Multiple p53 DNA Binding Domains: Insights into Loop 1 Intrinsic Dynamics. PLoS ONE. 8(11). e80221–e80221. 49 indexed citations
13.
Sim, Kelvin, Ghim-Eng Yap, David R. Hardoon, et al.. (2012). Centroid-Based Actionable 3D Subspace Clustering. IEEE Transactions on Knowledge and Data Engineering. 25(6). 1213–1226. 15 indexed citations
14.
Grant, Barry J., Suryani Lukman, Harrison J. Hocker, et al.. (2011). Novel Allosteric Sites on Ras for Lead Generation. PLoS ONE. 6(10). e25711–e25711. 138 indexed citations
15.
Lukman, Suryani, Robert Robinson, David J. Wales, & Chandra Verma. (2011). Conformational dynamics of capping protein and interaction partners: Simulation studies. Proteins Structure Function and Bioinformatics. 80(4). 1066–1077. 9 indexed citations
16.
Lukman, Suryani, Barry J. Grant, Alemayehu A. Gorfe, Guy H. Grant, & J. Andrew McCammon. (2010). The Distinct Conformational Dynamics of K-Ras and H-Ras A59G. PLoS Computational Biology. 6(9). e1000922–e1000922. 79 indexed citations
17.
Lukman, Suryani, Guy H. Grant, & Jennifer M. Bui. (2010). Unraveling evolutionary constraints: A heterogeneous conservation in dynamics of the titin Ig domains. FEBS Letters. 584(6). 1235–1239. 15 indexed citations
18.
Lukman, Suryani & Guy H. Grant. (2009). A network of dynamically conserved residues deciphers the motions of maltose transporter. Proteins Structure Function and Bioinformatics. 76(3). 588–597. 16 indexed citations
19.
Lukman, Suryani, Kelvin Sim, Jinyan Li, & Yi‐Ping Phoebe Chen. (2007). INTERACTING AMINO ACID PREFERENCES OF 3D PATTERN PAIRS AT THE BINDING SITES OF TRANSIENT AND OBLIGATE PROTEIN COMPLEXES. 69–78. 9 indexed citations
20.
Lukman, Suryani, et al.. (2007). Computational methods for Traditional Chinese Medicine: A survey. Computer Methods and Programs in Biomedicine. 88(3). 283–294. 143 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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