Ian P. Barrett

2.5k total citations
23 papers, 571 citations indexed

About

Ian P. Barrett is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Ian P. Barrett has authored 23 papers receiving a total of 571 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 10 papers in Computational Theory and Mathematics and 5 papers in Artificial Intelligence. Recurrent topics in Ian P. Barrett's work include Computational Drug Discovery Methods (10 papers), Bioinformatics and Genomic Networks (9 papers) and Congenital heart defects research (4 papers). Ian P. Barrett is often cited by papers focused on Computational Drug Discovery Methods (10 papers), Bioinformatics and Genomic Networks (9 papers) and Congenital heart defects research (4 papers). Ian P. Barrett collaborates with scholars based in United Kingdom, Sweden and Australia. Ian P. Barrett's co-authors include Ola Engkvist, Stephen Bonner, Andreas Bender, Alleyn T. Plowright, Qing‐Dong Wang, Lauren Drowley, Mei Ding, William L. Hamilton, Charles Tapley Hoyt and Cheng Ye and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Cancer Research.

In The Last Decade

Ian P. Barrett

22 papers receiving 567 citations

Peers

Ian P. Barrett

CTM

SURGE

ONCOL

Jiangming Sun Sweden

Yongchao Luo China

Harikrishna Paik India

Yin‐Ying Wang China

Xiang Zhuang China

Claire Jean-Quartier Austria

Uroš Kuzmanov Canada

Anna Niarakis France

Ammar Ammar Netherlands

Jaegyoon Ahn South Korea

Jiangming Sun Sweden

Ian P. Barrett

414 ×1.1

183 ×1.3

CTM

75 ×0.7

SURGE

36 ×0.4

31 ×0.5

ONCOL

Citations per year, relative to Ian P. Barrett Ian P. Barrett (= 1×) peers Jiangming Sun

Countries citing papers authored by Ian P. Barrett

Since Specialization

Citations

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

Fields of papers citing papers by Ian P. Barrett

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian P. Barrett. 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 Ian P. Barrett. The network helps show where Ian P. Barrett may publish in the future.

Co-authorship network of co-authors of Ian P. Barrett

This figure shows the co-authorship network connecting the top 25 collaborators of Ian P. Barrett. A scholar is included among the top collaborators of Ian P. Barrett 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 Ian P. Barrett. Ian P. Barrett 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

Tsafou, Kalliopi, et al.. (2025). Network-Based Approaches for Drug Target Identification. PubMed. 8(1). 423–446.

Farber, Emily, Ola Engkvist, Ian P. Barrett, et al.. (2024). Multi-omic analysis reveals VEGFR2, PI3K, and JNK mediate the small molecule induction of human iPSC-derived cardiomyocyte proliferation. iScience. 27(8). 110485–110485. 1 indexed citations

Dovedi, Simon J., Viia Valge-Archer, Amit Grover, et al.. (2023). Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma. Cancer Immunology Research. 11(8). 1125–1136. 2 indexed citations

Bonner, Stephen, Ian P. Barrett, Cheng Ye, et al.. (2022). Understanding the performance of knowledge graph embeddings in drug discovery. SHILAP Revista de lepidopterología. 2. 100036–100036. 34 indexed citations

Trapotsi, Maria‐Anna, Elizabeth Mouchet, Guy Williams, et al.. (2022). Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature. ACS Chemical Biology. 17(7). 1733–1744. 25 indexed citations

Bonner, Stephen, et al.. (2022). A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(6). 3070–3080. 8 indexed citations

Bonner, Stephen, Ian P. Barrett, Cheng Ye, et al.. (2022). A review of biomedical datasets relating to drug discovery: a knowledge graph perspective. Briefings in Bioinformatics. 23(6). 65 indexed citations

Bonner, Stephen, et al.. (2021). Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs. arXiv (Cornell University). 13 indexed citations

Mervin, Lewis, Maria‐Anna Trapotsi, Avid M. Afzal, et al.. (2021). Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty. Journal of Cheminformatics. 13(1). 62–62. 14 indexed citations

10.

Trapotsi, Maria‐Anna, Lewis Mervin, Avid M. Afzal, et al.. (2021). Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions. Journal of Chemical Information and Modeling. 61(3). 1444–1456. 25 indexed citations

11.

Bonner, Stephen, Ian P. Barrett, Cheng Ye, et al.. (2021). A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective. arXiv (Cornell University). 1 indexed citations

12.

Barrett, Ian P., et al.. (2020). Signaling Dynamics Regulating Crosstalks between T-Cell Activation and Immune Checkpoints. Trends in Cell Biology. 31(3). 224–235. 21 indexed citations

13.

Mills, Richard J., Benjamin L. Parker, Gregory A. Quaife-Ryan, et al.. (2019). Drug Screening in Human PSC-Cardiac Organoids Identifies Pro-proliferative Compounds Acting via the Mevalonate Pathway. Cell stem cell. 24(6). 895–907.e6. 205 indexed citations

14.

Ding, Mei, Alleyn T. Plowright, Ola Engkvist, et al.. (2018). High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets. Journal of Molecular and Cellular Cardiology. 127. 204–214. 21 indexed citations

15.

Gavaghan, David J., James M. Osborne, Ian P. Barrett, et al.. (2017). A mathematical model of antibody-dependent cellular cytotoxicity (ADCC). Journal of Theoretical Biology. 436. 39–50. 19 indexed citations

16.

Mervin, Lewis, Krishna C. Bulusu, Avid M. Afzal, et al.. (2017). Orthologue chemical space and its influence on target prediction. Bioinformatics. 34(1). 72–79. 23 indexed citations

17.

Barrett, Ian P., et al.. (2016). Kinase Inhibition Leads to Hormesis in a Dual Phosphorylation-Dephosphorylation Cycle. PLoS Computational Biology. 12(11). e1005216–e1005216. 11 indexed citations

18.

Mervin, Lewis, Qing Cao, Ian P. Barrett, et al.. (2016). Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection. ACS Chemical Biology. 11(11). 3007–3023. 32 indexed citations

19.

Karlin, Jeremy, Kurt G. Pike, Nicola Colclough, et al.. (2016). Abstract 3041: Blood-brain barrier penetrating ATM inhibitor (AZ32) radiosensitises intracranial gliomas in mice. Cancer Research. 76(14_Supplement). 3041–3041. 5 indexed citations

20.

Barrett, Ian P.. (2010). Cancer Genome Analysis Informatics. Methods in molecular biology. 628. 75–102. 6 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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