Sawsan Khuri

2.2k total citations
32 papers, 1.7k citations indexed

About

Sawsan Khuri is a scholar working on Molecular Biology, Plant Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Sawsan Khuri has authored 32 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 6 papers in Plant Science and 3 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Sawsan Khuri's work include Machine Learning in Bioinformatics (5 papers), Plant pathogens and resistance mechanisms (4 papers) and Bioinformatics and Genomic Networks (4 papers). Sawsan Khuri is often cited by papers focused on Machine Learning in Bioinformatics (5 papers), Plant pathogens and resistance mechanisms (4 papers) and Bioinformatics and Genomic Networks (4 papers). Sawsan Khuri collaborates with scholars based in United States, United Kingdom and Lebanon. Sawsan Khuri's co-authors include Jim M. Dunwell, A. C. Purvis, Paul J. Gane, Freek T. Bakker, Denis C. Guttridge, Andrea Bonetto, Leonidas G. Koniaris, Tufan Aydogdu, Teresa A. Zimmers and Noelia J. Kunzevitzky and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Microbiology and Molecular Biology Reviews.

In The Last Decade

Sawsan Khuri

31 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sawsan Khuri United States 18 973 596 222 126 107 32 1.7k
Hideo Tsuji Japan 31 1.8k 1.8× 1.4k 2.3× 122 0.5× 168 1.3× 51 0.5× 159 3.1k
Sho Tabata Japan 33 1.7k 1.7× 910 1.5× 69 0.3× 294 2.3× 61 0.6× 80 3.0k
He Liu China 27 1.4k 1.4× 1.2k 2.1× 73 0.3× 103 0.8× 56 0.5× 79 2.8k
Nana‐Maria Grüning Germany 15 1.2k 1.3× 201 0.3× 211 1.0× 98 0.8× 92 0.9× 20 2.1k
Anders L.B. Møller Denmark 9 1.0k 1.0× 840 1.4× 130 0.6× 61 0.5× 51 0.5× 12 1.8k
Takeshi Suzuki Japan 31 2.1k 2.2× 191 0.3× 143 0.6× 262 2.1× 38 0.4× 121 3.4k
Cinzia Franchin Italy 26 1.1k 1.1× 534 0.9× 122 0.5× 79 0.6× 65 0.6× 70 2.0k
Yingnan Chen China 21 928 1.0× 357 0.6× 24 0.1× 202 1.6× 111 1.0× 67 1.6k
Samuel Peña‐Llopis United States 25 1.5k 1.6× 229 0.4× 184 0.8× 159 1.3× 35 0.3× 43 3.0k
Takumi Nishiuchi Japan 32 1.7k 1.8× 1.8k 3.1× 81 0.4× 110 0.9× 49 0.5× 138 3.3k

Countries citing papers authored by Sawsan Khuri

Since Specialization
Citations

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

Fields of papers citing papers by Sawsan Khuri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sawsan Khuri

This figure shows the co-authorship network connecting the top 25 collaborators of Sawsan Khuri. A scholar is included among the top collaborators of Sawsan Khuri 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 Sawsan Khuri. Sawsan Khuri 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.
Taylor, Emma, et al.. (2025). Therapy gone viral: Exploring the use of oncolytic viruses in non-small cell lung cancer immunotherapy. Cancer Treatment and Research Communications. 44. 100971–100971.
2.
Khuri, Sawsan, et al.. (2019). Promoter conservation in HDACs points to functional implications. BMC Genomics. 20(1). 613–613. 12 indexed citations
3.
Greer, Justin B., Sawsan Khuri, & Lynne A. Fieber. (2017). Phylogenetic analysis of ionotropic L-glutamate receptor genes in the Bilateria, with special notes on Aplysia californica. BMC Evolutionary Biology. 17(1). 11–11. 21 indexed citations
4.
5.
Jiang, Zhijie, Derek Jones, Sawsan Khuri, et al.. (2013). Comparative analysis of genome sequences from four strains of the Buchnera aphidicola Mp endosymbion of the green peach aphid, Myzus persicae. BMC Genomics. 14(1). 917–917. 30 indexed citations
6.
Edwards, Yvonne J. K., Gary W. Beecham, William K. Scott, et al.. (2011). Identifying Consensus Disease Pathways in Parkinson's Disease Using an Integrative Systems Biology Approach. PLoS ONE. 6(2). e16917–e16917. 60 indexed citations
7.
Zaki, Nazar, et al.. (2011). Conotoxin protein classification using free scores of words and support vector machines. BMC Bioinformatics. 12(1). 217–217. 23 indexed citations
8.
Bonetto, Andrea, Tufan Aydogdu, Noelia J. Kunzevitzky, et al.. (2011). STAT3 Activation in Skeletal Muscle Links Muscle Wasting and the Acute Phase Response in Cancer Cachexia. PLoS ONE. 6(7). e22538–e22538. 280 indexed citations
9.
Rosero, Samuel, Valia Bravo-Egaña, Zhijie Jiang, et al.. (2010). MicroRNA signature of the human developing pancreas. BMC Genomics. 11(1). 509–509. 44 indexed citations
10.
Rampersaud, Evadnie, Daniel D. Kinnamon, Kara Hamilton, et al.. (2010). Common Susceptibility Variants Examined for Association with Dilated Cardiomyopathy. Annals of Human Genetics. 74(2). 110–116. 14 indexed citations
11.
Nassif, Houssam, et al.. (2010). An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge. Lecture notes in computer science. 5989. 149–165. 7 indexed citations
12.
Nassif, Houssam, et al.. (2009). Prediction of protein‐glucose binding sites using support vector machines. Proteins Structure Function and Bioinformatics. 77(1). 121–132. 26 indexed citations
13.
Radwan, Ahmed, Akmal A. Younis, Peter Luykx, & Sawsan Khuri. (2008). Prediction and analysis of nucleosome exclusion regions in the human genome. BMC Genomics. 9(1). 186–186. 20 indexed citations
14.
Radwan, Ahmed, Akmal A. Younis, Sawsan Khuri, et al.. (2007). BioFederator: A Data Federation System for Bioinformatics on the Web. 12(2). 31–9. 2 indexed citations
15.
Luykx, Peter, Ivan V. Bajić, & Sawsan Khuri. (2006). NXSensor web tool for evaluating DNA for nucleosome exclusion sequences and accessibility to binding factors. Nucleic Acids Research. 34(Web Server). W560–W565. 7 indexed citations
16.
Mnayer, Laila, Sawsan Khuri, Hassan Al‐Ali, Germana Meroni, & Louis J. Elsas. (2005). A structure–function study of MID1 mutations associated with a mild Opitz phenotype. Molecular Genetics and Metabolism. 87(3). 198–203. 29 indexed citations
17.
Gibbings, J. George, Brian P. Cook, Michael R. Dufault, et al.. (2003). Global transcript analysis of rice leaf and seed using SAGE technology. Plant Biotechnology Journal. 1(4). 271–285. 56 indexed citations
18.
Talhouk, Salma N., Rami Zurayk, & Sawsan Khuri. (2003). CONIFER CONSERVATION IN LEBANON. Acta Horticulturae. 411–414. 4 indexed citations
19.
Dunwell, Jim M., A. C. Purvis, & Sawsan Khuri. (2003). Cupins: the most functionally diverse protein superfamily?. Phytochemistry. 65(1). 7–17. 436 indexed citations
20.
Khuri, Sawsan, Freek T. Bakker, & Jim M. Dunwell. (2001). Phylogeny, Function, and Evolution of the Cupins, a Structurally Conserved, Functionally Diverse Superfamily of Proteins. Molecular Biology and Evolution. 18(4). 593–605. 133 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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