Barry Hardy

2.1k total citations
21 papers, 606 citations indexed

About

Barry Hardy is a scholar working on Computational Theory and Mathematics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Barry Hardy has authored 21 papers receiving a total of 606 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Theory and Mathematics, 13 papers in Molecular Biology and 4 papers in Artificial Intelligence. Recurrent topics in Barry Hardy's work include Computational Drug Discovery Methods (14 papers), Biomedical Text Mining and Ontologies (5 papers) and Statistical and Computational Modeling (4 papers). Barry Hardy is often cited by papers focused on Computational Drug Discovery Methods (14 papers), Biomedical Text Mining and Ontologies (5 papers) and Statistical and Computational Modeling (4 papers). Barry Hardy collaborates with scholars based in Sweden, United Kingdom and United States. Barry Hardy's co-authors include Richard W. Pastor, Yuhong Zhang, Richard M. Venable, Egon Willighagen, Pekka Kohonen, Nina Jeliazkova, Roland Grafström, Ola Spjuth, Vedrin Jeliazkov and Rebecca Ceder and has published in prestigious journals such as Science, Journal of Computational Chemistry and Drug Discovery Today.

In The Last Decade

Barry Hardy

19 papers receiving 585 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Barry Hardy Sweden 12 383 154 122 81 65 21 606
Greg M. Pearl United States 11 155 0.4× 49 0.3× 161 1.3× 78 1.0× 60 0.9× 13 574
Jörg Bentzien United States 15 324 0.8× 82 0.5× 134 1.1× 70 0.9× 25 0.4× 24 739
Pil H. Lee United States 12 182 0.5× 50 0.3× 133 1.1× 48 0.6× 50 0.8× 23 449
Yun-De Xiao United States 13 341 0.9× 36 0.2× 369 3.0× 64 0.8× 25 0.4× 18 675
Stefano Bosisio Italy 11 189 0.5× 46 0.3× 80 0.7× 96 1.2× 20 0.3× 16 358
Stefan Güssregen Germany 17 308 0.8× 85 0.6× 182 1.5× 143 1.8× 25 0.4× 33 924
Clayton Springer United States 16 200 0.5× 205 1.3× 149 1.2× 79 1.0× 23 0.4× 29 711
Kiyoshi Sasaki Japan 18 217 0.6× 73 0.5× 30 0.2× 45 0.6× 31 0.5× 44 746
Stephan Reiling United States 16 522 1.4× 120 0.8× 419 3.4× 181 2.2× 74 1.1× 21 1.0k
Eleanore Seibert United States 11 612 1.6× 44 0.3× 55 0.5× 66 0.8× 51 0.8× 13 943

Countries citing papers authored by Barry Hardy

Since Specialization
Citations

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

Fields of papers citing papers by Barry Hardy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Barry Hardy

This figure shows the co-authorship network connecting the top 25 collaborators of Barry Hardy. A scholar is included among the top collaborators of Barry Hardy 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 Barry Hardy. Barry Hardy 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.
Braak, Bas ter, Steven J. Kunnen, Barira Islam, et al.. (2025). Transcriptomic changes and mitochondrial toxicity in response to acute and repeat dose treatment with brequinar in human liver and kidney in vitro models. Toxicology in Vitro. 104. 106010–106010.
2.
Kirchmair, Johannes, Andreas Schepky, Jochen Kühnl, et al.. (2024). Increasing Accessibility of Bayesian Network-Based Defined Approaches for Skin Sensitisation Potency Assessment. Toxics. 12(9). 666–666. 1 indexed citations
3.
Hardy, Barry, et al.. (2022). Semi-automated approach for generation of biological networks on drug-induced cholestasis, steatosis, hepatitis, and cirrhosis. Toxicological Research. 38(3). 393–407. 4 indexed citations
4.
Chandrasekaran, Vidya, et al.. (2021). Temporal transcriptomic alterations of cadmium exposed human iPSC-derived renal proximal tubule-like cells. Toxicology in Vitro. 76. 105229–105229. 12 indexed citations
5.
Doktorova, Tatyana Y., et al.. (2020). A semi-automated workflow for adverse outcome pathway hypothesis generation: The use case of non-genotoxic induced hepatocellular carcinoma. Regulatory Toxicology and Pharmacology. 114. 104652–104652. 5 indexed citations
6.
7.
Exner, Thomas E., Lucian Farcal, Chris T. Evelo, et al.. (2018). OpenRiskNet, an open e-infrastructure to support data sharing, knowledge integration and in silico analysis and modelling in risk assessment. Toxicology Letters. 295. S104–S104. 1 indexed citations
8.
Bañares, Miguel Á., Andrea Haase, Lang Tran, et al.. (2017). CompNanoTox2015: novel perspectives from a European conference on computational nanotoxicology on predictive nanotoxicology. Nanotoxicology. 11(7). 839–845. 13 indexed citations
9.
Jeliazkova, Nina, Philip Doganis, Bengt Fadeel, et al.. (2015). The eNanoMapper database for nanomaterial safety information. Beilstein Journal of Nanotechnology. 6. 1609–1634. 79 indexed citations
10.
Grafström, Roland, Penny Nymark, Vesa Hongisto, et al.. (2015). Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of ‘Omics’ Data from Human Cell Cultures. Alternatives to Laboratory Animals. 43(5). 325–332. 20 indexed citations
11.
Kohonen, Pekka, Rebecca Ceder, Ines Smit, et al.. (2014). Cancer Biology, Toxicology and Alternative Methods Development Go Hand‐in‐Hand. Basic & Clinical Pharmacology & Toxicology. 115(1). 50–58. 18 indexed citations
12.
Hardy, Barry, et al.. (2013). Collaborative virtual organisation and infrastructure for drug discovery. Drug Discovery Today. 18(13-14). 681–686. 2 indexed citations
13.
Kohonen, Pekka, Emilio Benfenati, Rebecca Ceder, et al.. (2013). The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing. Molecular Informatics. 32(1). 47–63. 24 indexed citations
14.
Hardy, Barry. (2012). Toxicology ontology perspectives. ALTEX. 29(2). 139–156. 29 indexed citations
15.
Tcheremenskaia, Olga, Ivelina Nikolova, Nina Jeliazkova, et al.. (2012). OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia. Journal of Biomedical Semantics. 3(Suppl 1). S7–S7. 21 indexed citations
16.
Hardy, Barry. (2012). A toxicology ontology roadmap. ALTEX. 29(2). 129–137. 20 indexed citations
17.
Willighagen, Egon, Nina Jeliazkova, Barry Hardy, Roland C. Grafström, & Ola Spjuth. (2011). Computational toxicology using the OpenTox application programming interface and Bioclipse. BMC Research Notes. 4(1). 487–487. 15 indexed citations
18.
Hardy, Barry. (2009). Growing Significance of Communities and Collaboration in Discovery and Development. Future Medicinal Chemistry. 1(3). 435–449. 4 indexed citations
19.
Ishikawa, Satoru, K. Toriyama, K. L. Heong, & Barry Hardy. (2005). Promising technologies for reducing cadmium contamination in rice.. 381–384. 1 indexed citations
20.
Venable, Richard M., Yuhong Zhang, Barry Hardy, & Richard W. Pastor. (1993). Molecular Dynamics Simulations of a Lipid Bilayer and of Hexadecane: An Investigation of Membrane Fluidity. Science. 262(5131). 223–226. 306 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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