Jean-Paul Ebejer

716 total citations
20 papers, 459 citations indexed

About

Jean-Paul Ebejer is a scholar working on Molecular Biology, Computational Theory and Mathematics and Information Systems. According to data from OpenAlex, Jean-Paul Ebejer has authored 20 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 8 papers in Computational Theory and Mathematics and 4 papers in Information Systems. Recurrent topics in Jean-Paul Ebejer's work include Computational Drug Discovery Methods (8 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Machine Learning in Materials Science (3 papers). Jean-Paul Ebejer is often cited by papers focused on Computational Drug Discovery Methods (8 papers), Metabolomics and Mass Spectrometry Studies (3 papers) and Machine Learning in Materials Science (3 papers). Jean-Paul Ebejer collaborates with scholars based in Malta, United Kingdom and Latvia. Jean-Paul Ebejer's co-authors include Charlotte M. Deane, Garrett M. Morris, Paul W. Finn, M. Charlton, Gillian M. Martin, Simone Fulle, Jiye Shi, Sebastian Kelm, Maria Penzo and Aigars Jirgensons and has published in prestigious journals such as Nucleic Acids Research, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Jean-Paul Ebejer

17 papers receiving 449 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jean-Paul Ebejer Malta	9	197	184	98	67	52	20	459
Prasenjit Mukherjee United States	14	369 1.9×	404 2.2×	92 0.9×	103 1.5×	105 2.0×	47	751
Elizabeth Durham United States	7	193 1.0×	66 0.4×	39 0.4×	28 0.4×	40 0.8×	7	505
Sankalp Jain United States	12	161 0.8×	185 1.0×	35 0.4×	25 0.4×	21 0.4×	52	473
Karina Machado Brazil	14	201 1.0×	88 0.5×	56 0.6×	12 0.2×	51 1.0×	60	455
Indira Ghosh India	14	343 1.7×	92 0.5×	61 0.6×	70 1.0×	47 0.9×	43	574
Masakazu Sekijima Japan	13	518 2.6×	305 1.7×	201 2.1×	23 0.3×	79 1.5×	60	808
Maria Voigt United States	9	359 1.8×	127 0.7×	94 1.0×	16 0.2×	43 0.8×	13	571
Wladimiro Dı́az-Villanueva Spain	12	125 0.6×	102 0.6×	34 0.3×	8 0.1×	44 0.8×	31	413
Lucianna Helene Santos Brazil	12	369 1.9×	133 0.7×	57 0.6×	52 0.8×	63 1.2×	38	617
Andreas Martin Lisewski United States	14	461 2.3×	95 0.5×	73 0.7×	61 0.9×	9 0.2×	22	722

Countries citing papers authored by Jean-Paul Ebejer

Since Specialization

Citations

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

Fields of papers citing papers by Jean-Paul Ebejer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jean-Paul Ebejer. 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 Jean-Paul Ebejer. The network helps show where Jean-Paul Ebejer may publish in the future.

Co-authorship network of co-authors of Jean-Paul Ebejer

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

All Works

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20 of 20 papers shown

Barzine, Mitra, et al.. (2024). Isolating high-quality RNA for RNA-Seq from 10-year-old blood samples. Scientific Reports. 14(1). 30716–30716.

Ebejer, Jean-Paul, et al.. (2022). Machine Learning Using Neural Networks for Metabolomic Pathway Analyses. Methods in molecular biology. 2553. 395–415. 3 indexed citations

Ebejer, Jean-Paul, et al.. (2022). Few-Shot Learning for Low-Data Drug Discovery. Journal of Chemical Information and Modeling. 63(1). 27–42. 33 indexed citations

Azzopardi, Joseph & Jean-Paul Ebejer. (2021). LigityScore: Convolutional Neural Network for Binding-affinity Predictions. 38–49.

Azzopardi, Joseph & Jean-Paul Ebejer. (2021). LigityScore: Convolutional Neural Network for Binding-affinity Predictions. 38–49. 5 indexed citations

Ebejer, Jean-Paul, et al.. (2021). Possible Role of Circulating Bone Marrow Mesenchymal Progenitors in Modulating Inflammation and Promoting Wound Repair. International Journal of Molecular Sciences. 23(1). 78–78. 2 indexed citations

Ebejer, Jean-Paul, et al.. (2020). Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening. Frontiers in Pharmacology. 10. 1675–1675. 18 indexed citations

Martin, Gillian M., et al.. (2019). Dwarna: a blockchain solution for dynamic consent in biobanking. European Journal of Human Genetics. 28(5). 609–626. 56 indexed citations

Ebejer, Jean-Paul, Paul W. Finn, Wing Ki Wong, Charlotte M. Deane, & Garrett M. Morris. (2019). Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening. Journal of Chemical Information and Modeling. 59(6). 2600–2616. 7 indexed citations

10.

Colombo, Christian, et al.. (2018). Runtime Verification using VALOUR. 3. 10–0. 3 indexed citations

11.

Pace, Gordon J., et al.. (2017). Engineering Adaptive User Interfaces Using Monitoring-Oriented Programming. OAR@UM (University of Malta). 5596. 200–207. 2 indexed citations

12.

Ebejer, Jean-Paul, M. Charlton, & Paul W. Finn. (2016). Are the physicochemical properties of antibacterial compounds really different from other drugs?. Journal of Cheminformatics. 8(1). 30–30. 46 indexed citations

13.

Ebejer, Jean-Paul, et al.. (2016). Role of protein structure in drug discovery. OAR@UM (University of Malta).

14.

Penzo, Maria, Simone Fulle, Jean-Paul Ebejer, et al.. (2014). Quinoxaline-Based Inhibitors of Malarial Protease PfSUB1*. Chemistry of Heterocyclic Compounds. 50(10). 1457–1463. 10 indexed citations

15.

Wilman, H., Jean-Paul Ebejer, Jiye Shi, Charlotte M. Deane, & Bernhard Knapp. (2014). Crowdsourcing Yields a New Standard for Kinks in Protein Helices. Journal of Chemical Information and Modeling. 54(9). 2585–2593. 6 indexed citations

16.

Ebejer, Jean-Paul, Simone Fulle, Garrett M. Morris, & Paul W. Finn. (2013). The emerging role of cloud computing in molecular modelling. Journal of Molecular Graphics and Modelling. 44. 177–187. 19 indexed citations

17.

Ebejer, Jean-Paul, et al.. (2013). Memoir: template-based structure prediction for membrane proteins. Nucleic Acids Research. 41(W1). W379–W383. 33 indexed citations

18.

Kelm, Sebastian, Anna Vangone, Yoonjoo Choi, et al.. (2013). Fragment-based modeling of membrane protein loops: Successes, failures, and prospects for the future. Proteins Structure Function and Bioinformatics. 82(2). 175–186. 7 indexed citations

19.

Withers‐Martinez, Chrislaine, Catherine Suárez, Simone Fulle, et al.. (2012). Plasmodium subtilisin-like protease 1 (SUB1): Insights into the active-site structure, specificity and function of a pan-malaria drug target. International Journal for Parasitology. 42(6). 597–612. 46 indexed citations

20.

Ebejer, Jean-Paul, Garrett M. Morris, & Charlotte M. Deane. (2012). Freely Available Conformer Generation Methods: How Good Are They?. Journal of Chemical Information and Modeling. 52(5). 1146–1158. 163 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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