John Washbrook

588 total citations
21 papers, 414 citations indexed

About

John Washbrook is a scholar working on Molecular Biology, Artificial Intelligence and Information Systems. According to data from OpenAlex, John Washbrook has authored 21 papers receiving a total of 414 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 8 papers in Artificial Intelligence and 3 papers in Information Systems. Recurrent topics in John Washbrook's work include AI-based Problem Solving and Planning (8 papers), Viral Infectious Diseases and Gene Expression in Insects (7 papers) and Biomedical Text Mining and Ontologies (4 papers). John Washbrook is often cited by papers focused on AI-based Problem Solving and Planning (8 papers), Viral Infectious Diseases and Gene Expression in Insects (7 papers) and Biomedical Text Mining and Ontologies (4 papers). John Washbrook collaborates with scholars based in United Kingdom and Cyprus. John Washbrook's co-authors include Suzanne S. Farid, Nigel J. Titchener‐Hooker, Elpida Keravnou, Yuhong Zhou, Andrew Sinclair, Brendan Fish, Mustafa Abbas Mustafa, M. Stone, C. Michael Hall and D. Shaw and has published in prestigious journals such as Biotechnology and Bioengineering, Knowledge-Based Systems and Computers & Chemical Engineering.

In The Last Decade

John Washbrook

21 papers receiving 384 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Washbrook United Kingdom 11 224 92 80 56 49 21 414
Nilou Arden United States 9 310 1.4× 14 0.2× 31 0.4× 49 0.9× 94 1.9× 12 583
J. Wojciechowski Poland 10 66 0.3× 35 0.4× 72 0.9× 38 0.7× 9 0.2× 67 426
Shinsuke Tamura Japan 11 125 0.6× 92 1.0× 37 0.5× 7 0.1× 24 0.5× 51 358
Xiangru Chen China 9 113 0.5× 111 1.2× 9 0.1× 16 0.3× 19 0.4× 40 464
Maria M. Papathanasiou United Kingdom 15 262 1.2× 11 0.1× 269 3.4× 49 0.9× 106 2.2× 42 691
Yaojun Wang China 15 97 0.4× 34 0.4× 38 0.5× 6 0.1× 35 0.7× 49 603
Girish Joglekar United States 6 39 0.2× 37 0.4× 79 1.0× 3 0.1× 24 0.5× 11 238
Yingsheng Zhang China 13 205 0.9× 34 0.4× 14 0.2× 4 0.1× 17 0.3× 35 520
Haixin Wang China 13 140 0.6× 105 1.1× 10 0.1× 28 0.5× 15 0.3× 41 557
Sukhpreet Kaur India 9 89 0.4× 108 1.2× 11 0.1× 28 0.5× 24 0.5× 30 352

Countries citing papers authored by John Washbrook

Since Specialization
Citations

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

Fields of papers citing papers by John Washbrook

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Washbrook

This figure shows the co-authorship network connecting the top 25 collaborators of John Washbrook. A scholar is included among the top collaborators of John Washbrook 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 John Washbrook. John Washbrook 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.
Farid, Suzanne S., John Washbrook, & Nigel J. Titchener‐Hooker. (2006). Modelling biopharmaceutical manufacture: Design and implementation of SimBiopharma. Computers & Chemical Engineering. 31(9). 1141–1158. 38 indexed citations
2.
Mustafa, Mustafa Abbas, John Washbrook, Nigel J. Titchener‐Hooker, & Suzanne S. Farid. (2006). Retrofit Decisions within the Biopharmaceutical Industry. Food and Bioproducts Processing. 84(1). 84–89. 2 indexed citations
3.
Washbrook, John, et al.. (2005). A software tool to assist business-process decision-making in the bio-pharmaceutical industry (vol 20, pg 1096, 2004). UCL Discovery (University College London). 1 indexed citations
4.
Farid, Suzanne S., John Washbrook, & Nigel J. Titchener‐Hooker. (2005). Decision-Support Tool for Assessing Biomanufacturing Strategies under Uncertainty: Stainless Steel versus Disposable Equipment for Clinical Trial Material Preparation. Biotechnology Progress. 21(2). 486–497. 77 indexed citations
5.
Farid, Suzanne S., John Washbrook, & Nigel J. Titchener‐Hooker. (2005). Combining Multiple Quantitative and Qualitative Goals When Assessing Biomanufacturing Strategies under Uncertainty. Biotechnology Progress. 21(4). 1183–1191. 26 indexed citations
6.
Washbrook, John, et al.. (2005). A computer‐aided approach to compare the production economics of fed‐batch and perfusion culture under uncertainty. Biotechnology and Bioengineering. 93(4). 687–697. 51 indexed citations
7.
Zhou, Yuhong, John Washbrook, Andrew Sinclair, et al.. (2005). Application of a Decision-Support Tool to Assess Pooling Strategies in Perfusion Culture Processes under Uncertainty. Biotechnology Progress. 21(4). 1231–1242. 40 indexed citations
8.
Mustafa, Mustafa Abbas, et al.. (2004). . Biotechnology Progress. 20(4). 1096–1102. 15 indexed citations
9.
Zhou, Yuhong, et al.. (2004). A decisional-support tool to model the impact of regulatory compliance activities in the biomanufacturing industry. Computers & Chemical Engineering. 28(5). 727–735. 20 indexed citations
10.
Davies, Elwyn, et al.. (2001). Biopharmaceutical process development: Part III, A framework to assist decision making. UCL Discovery (University College London). 3 indexed citations
11.
Keravnou, Elpida & John Washbrook. (2001). Abductive Diagnosis Using Time‐Objects: Criteria for the Evaluation of Solutions. Computational Intelligence. 17(1). 87–131. 3 indexed citations
12.
Farid, Suzanne S., et al.. (2000). A Tool for Modeling Strategic Decisions in Cell Culture Manufacturing. Biotechnology Progress. 16(5). 829–836. 27 indexed citations
13.
Woodley, John M., et al.. (1995). Design of biotransformation processes: use of a knowledge-based system. 73. 133–139. 2 indexed citations
14.
Keravnou, Elpida, et al.. (1994). Modelling diagnostic skills in the domain of skeletal dysplasias. Computer Methods and Programs in Biomedicine. 45(4). 239–260. 8 indexed citations
15.
Keravnou, Elpida, et al.. (1993). Towards competent information acquisition interactions between an expert system and its user. Knowledge-Based Systems. 6(3). 141–156. 2 indexed citations
16.
Keravnou, Elpida, et al.. (1992). Background knowledge in diagnosis. Artificial Intelligence in Medicine. 4(4). 263–279. 6 indexed citations
17.
Washbrook, John & Elpida Keravnou. (1990). Making deepness explicit. Artificial Intelligence in Medicine. 2(3). 129–134. 6 indexed citations
18.
Keravnou, Elpida & John Washbrook. (1990). A temporal reasoning framework used in the diagnosis of skeletal dysplasias. Artificial Intelligence in Medicine. 2(5). 239–265. 29 indexed citations
19.
Keravnou, Elpida & John Washbrook. (1989). What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems. The Knowledge Engineering Review. 4(3). 205–233. 28 indexed citations
20.
Stone, M., et al.. (1980). Cross-Validatory Selection of Binary Variables in Differential Diagnosis. Journal of the Royal Statistical Society Series C (Applied Statistics). 29(2). 198–198. 7 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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