Sandhya Samarasinghe

3.0k total citations
116 papers, 2.2k citations indexed

About

Sandhya Samarasinghe is a scholar working on Molecular Biology, Artificial Intelligence and Plant Science. According to data from OpenAlex, Sandhya Samarasinghe has authored 116 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 19 papers in Artificial Intelligence and 14 papers in Plant Science. Recurrent topics in Sandhya Samarasinghe's work include Gene Regulatory Network Analysis (17 papers), Bioinformatics and Genomic Networks (12 papers) and Wood Treatment and Properties (10 papers). Sandhya Samarasinghe is often cited by papers focused on Gene Regulatory Network Analysis (17 papers), Bioinformatics and Genomic Networks (12 papers) and Wood Treatment and Properties (10 papers). Sandhya Samarasinghe collaborates with scholars based in New Zealand, United States and Jordan. Sandhya Samarasinghe's co-authors include Don Kulasiri, Majeed Safa, Subana Shanmuganathan, Jennifer Salmond, Kim N. Dirks, Naresh Singhal, Hong Ling, J. Jago, Zhibin Sun and R. Bickerstaffe and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Sandhya Samarasinghe

105 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sandhya Samarasinghe New Zealand 23 360 334 282 220 209 116 2.2k
Yingyi Chen China 28 594 1.6× 359 1.1× 337 1.2× 510 2.3× 221 1.1× 155 3.2k
Qian Zhang China 28 333 0.9× 485 1.5× 131 0.5× 293 1.3× 307 1.5× 175 3.8k
José García‐Rodríguez Spain 21 397 1.1× 604 1.8× 163 0.6× 158 0.7× 138 0.7× 124 3.3k
Qiaolin Ye China 30 221 0.6× 974 2.9× 299 1.1× 166 0.8× 144 0.7× 153 3.2k
Maha N. Hajmeer United States 17 268 0.7× 537 1.6× 231 0.8× 142 0.6× 95 0.5× 31 3.3k
Min Zuo China 20 331 0.9× 290 0.9× 106 0.4× 375 1.7× 120 0.6× 75 2.0k
Zihao Wu China 23 260 0.7× 505 1.5× 89 0.3× 222 1.0× 129 0.6× 105 2.0k
Jesús González Spain 30 181 0.5× 817 2.4× 331 1.2× 292 1.3× 319 1.5× 169 3.1k
Tzu-Tsung Wong Taiwan 12 153 0.4× 593 1.8× 207 0.7× 73 0.3× 94 0.4× 19 2.2k
Ioannis Dimopoulos Greece 11 511 1.4× 328 1.0× 71 0.3× 111 0.5× 402 1.9× 22 2.2k

Countries citing papers authored by Sandhya Samarasinghe

Since Specialization
Citations

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

Fields of papers citing papers by Sandhya Samarasinghe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sandhya Samarasinghe

This figure shows the co-authorship network connecting the top 25 collaborators of Sandhya Samarasinghe. A scholar is included among the top collaborators of Sandhya Samarasinghe 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 Sandhya Samarasinghe. Sandhya Samarasinghe 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.
Samarasinghe, Sandhya, et al.. (2023). Production of plant-based milk from local almond nuts (<em>Terminalia catappa L</em>.) and evaluation of its sensory and nutritional properties. Tropical Agricultural Research and Extension. 26(2). 104–110.
2.
Samarasinghe, Sandhya, et al.. (2023). Proteins as fuzzy controllers: Auto tuning a biological fuzzy inference system to predict protein dynamics in complex biological networks. Biosystems. 224. 104826–104826. 2 indexed citations
3.
Anthony, Patricia, et al.. (2022). Water distribution in community irrigation using a multi‐agent system. Journal of the Royal Society of New Zealand. 53(1). 6–26. 3 indexed citations
4.
Samarasinghe, Sandhya. (2022). Collective computational intelligence in biology – Emergence of memory in somatic tissues. Biosystems. 223. 104816–104816.
5.
Samarasinghe, Sandhya, et al.. (2021). A Comprehensive Conceptual and Computational Dynamics Framework for Autonomous Regeneration Systems. Artificial Life. 27(2). 80–104. 3 indexed citations
6.
Samarasinghe, Sandhya, et al.. (2019). Detection of dairy cattle Mastitis: modelling of milking features using deep neural networks. Lincoln University Research Archive (Lincoln University). 6 indexed citations
7.
Samarasinghe, Sandhya, et al.. (2019). An improved stochastic modelling framework for biological networks. Lincoln University Research Archive (Lincoln University). 1 indexed citations
8.
Kulasiri, Don, et al.. (2018). A Novel Data-Driven Boolean Model for Genetic Regulatory Networks. Frontiers in Physiology. 9. 1328–1328. 10 indexed citations
9.
10.
Samarasinghe, Sandhya, et al.. (2017). Modelling a multi agent system for dairy farms for integrated decision making. 2 indexed citations
11.
Samarasinghe, Sandhya, et al.. (2015). A review of computational models of mammalian cell cycle. Lincoln University Research Archive (Lincoln University). 1 indexed citations
12.
Samarasinghe, Sandhya, et al.. (2015). Computational techniques in mathematical modelling of biological switches. DR-NTU (Nanyang Technological University). 1 indexed citations
13.
Shokouhifar, Mohammad, et al.. (2015). On the use of local and global search paradigms for computer-aided diagnosis of breast cancer. Lincoln University Research Archive (Lincoln University). 1 indexed citations
14.
Safa, Majeed, et al.. (2015). Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand. Journal of Agricultural Science and Technology. 17(4). 791–803. 48 indexed citations
15.
Samarasinghe, Sandhya, et al.. (2011). Ultrasound based computer aided diagnosis of breast cancer: Evaluation of a new feature of mass central regularity degree. Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation.. 2 indexed citations
16.
Cominetti, Ornella, Anastasios Matzavinos, Sandhya Samarasinghe, et al.. (2009). DifFUZZY: A fuzzy spectral clustering algorithm for complex data sets. Oxford University Research Archive (ORA) (University of Oxford). 1 indexed citations
17.
Samarasinghe, Sandhya, et al.. (2007). The use of neural networks to detect minor and major pathogens that cause bovine mastitis. Proceedings of the New Zealand Society of Animal Production. 67. 215–219. 1 indexed citations
18.
Ling, Hong, Sandhya Samarasinghe, & Don Kulasiri. (2007). Modelling displacement fields of wood in compression loading using stochastic neural networks. Lincoln University Research Archive (Lincoln University). 1 indexed citations
19.
Samarasinghe, Sandhya, et al.. (1997). A Network of Neural Nets to Model Power System Networks for Fault Diagnosis.. International Conference on Neural Information Processing. 1004–1007. 2 indexed citations
20.
Samarasinghe, Sandhya, Joseph R. Loferski, & Siegfried M. Holzer. (1994). CREEP MODELING OF WOOD USING TIME-TEMPERATURE SUPERPOSITION. Wood and Fiber Science. 26(1). 122–130. 11 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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