Sujan Perera

412 total citations
10 papers, 246 citations indexed

About

Sujan Perera is a scholar working on Artificial Intelligence, Molecular Biology and Toxicology. According to data from OpenAlex, Sujan Perera has authored 10 papers receiving a total of 246 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Molecular Biology and 3 papers in Toxicology. Recurrent topics in Sujan Perera's work include Biomedical Text Mining and Ontologies (4 papers), Topic Modeling (4 papers) and Pharmacovigilance and Adverse Drug Reactions (3 papers). Sujan Perera is often cited by papers focused on Biomedical Text Mining and Ontologies (4 papers), Topic Modeling (4 papers) and Pharmacovigilance and Adverse Drug Reactions (3 papers). Sujan Perera collaborates with scholars based in United States, Switzerland and Sri Lanka. Sujan Perera's co-authors include Amit Sheth, Robert G. Carlson, Raminta Daniulaityte, Lu Chen, Delroy Cameron, Rüssel S. Falck, Krishnaprasad Thirunarayan, Jennifer Fine, C. R. Ramakrishnan and Meenakshi Nagarajan and has published in prestigious journals such as Drug and Alcohol Dependence, Drug Safety and IEEE Journal of Biomedical and Health Informatics.

In The Last Decade

Sujan Perera

10 papers receiving 233 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sujan Perera United States	7	73	71	52	45	34	10	246
Brant Chee United States	7	72 1.0×	29 0.4×	82 1.6×	77 1.7×	75 2.2×	16	421
Joseph M. Plasek United States	13	81 1.1×	7 0.1×	127 2.4×	108 2.4×	55 1.6×	42	544
David J. Lewis Switzerland	12	226 3.1×	8 0.1×	16 0.3×	49 1.1×	98 2.9×	45	523
Delroy Cameron United States	8	37 0.5×	72 1.0×	148 2.8×	125 2.8×	57 1.7×	17	379
Krystl Haerian United States	9	232 3.2×	5 0.1×	112 2.2×	158 3.5×	35 1.0×	10	488
Tanya Podchiyska United States	7	61 0.8×	5 0.1×	169 3.3×	129 2.9×	50 1.5×	10	389
Daniel R. Harris United States	11	29 0.4×	15 0.2×	55 1.1×	131 2.9×	69 2.0×	52	448
Daniele Sartori Sweden	7	149 2.0×	5 0.1×	7 0.1×	17 0.4×	28 0.8×	16	255
Stephanie J. Reisinger United States	9	142 1.9×	3 0.0×	40 0.8×	80 1.8×	18 0.5×	10	322
Benjamin Kompa United States	6	26 0.4×	11 0.2×	128 2.5×	30 0.7×	15 0.4×	8	338

Countries citing papers authored by Sujan Perera

Since Specialization

Citations

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

Fields of papers citing papers by Sujan Perera

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sujan Perera

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

All Works

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10 of 10 papers shown

1.

Perera, Sujan, et al.. (2019). Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation. Pharmaceutical Medicine. 33(2). 109–120. 32 indexed citations

2.

Perera, Sujan, et al.. (2018). Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing. Pharmaceutical Medicine. 32(6). 391–401. 22 indexed citations

3.

Perera, Sujan, et al.. (2018). Sorting Through the Safety Data Haystack: Using Machine Learning to Identify Individual Case Safety Reports in Social-Digital Media. Drug Safety. 41(6). 579–590. 32 indexed citations

4.

Perera, Sujan, et al.. (2017). Domain-specific hierarchical subgraph extraction: A recommendation use case. 666–675. 5 indexed citations

5.

Weerasinghe, Ruvan, et al.. (2016). Knowledge-Driven Approach to Predict Personality Traits by Leveraging Social Media Data. 8266 lnai. 288–295. 6 indexed citations

6.

Perera, Sujan, et al.. (2015). Implicit Entity Recognition in Clinical Documents. 228–238. 5 indexed citations

7.

Perera, Sujan, et al.. (2013). Challenges in understanding clinical notes. Journal of Bioresource Management. 21–26. 16 indexed citations

8.

Perera, Sujan, et al.. (2013). Semantics Driven Approach for Knowledge Acquisition From EMRs. IEEE Journal of Biomedical and Health Informatics. 18(2). 515–524. 13 indexed citations

9.

Daniulaityte, Raminta, Robert G. Carlson, Rüssel S. Falck, et al.. (2012). “I just wanted to tell you that loperamide WILL WORK”: A web-based study of extra-medical use of loperamide. Drug and Alcohol Dependence. 130(1-3). 241–244. 106 indexed citations

10.

Perera, Sujan, et al.. (2012). Data driven knowledge acquisition method for domain knowledge enrichment in the healthcare. Journal of Bioresource Management. 5383. 1–8. 9 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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