Timothy E. H. Allen

1.2k total citations
26 papers, 668 citations indexed

About

Timothy E. H. Allen is a scholar working on Computational Theory and Mathematics, Molecular Biology and Genetics. According to data from OpenAlex, Timothy E. H. Allen has authored 26 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Theory and Mathematics, 13 papers in Molecular Biology and 5 papers in Genetics. Recurrent topics in Timothy E. H. Allen's work include Computational Drug Discovery Methods (16 papers), RNA and protein synthesis mechanisms (6 papers) and Bacterial Genetics and Biotechnology (4 papers). Timothy E. H. Allen is often cited by papers focused on Computational Drug Discovery Methods (16 papers), RNA and protein synthesis mechanisms (6 papers) and Bacterial Genetics and Biotechnology (4 papers). Timothy E. H. Allen collaborates with scholars based in United Kingdom, United States and Netherlands. Timothy E. H. Allen's co-authors include Jonathan M. Goodman, Steve Gutsell, Paul Russell, Bernhard Ø. Palsson, Elenita I. Kanin, Robert Landick, Christopher D. Herring, Aseem Z. Ansari, Nathan D. Price and Andrew R. Joyce and has published in prestigious journals such as Angewandte Chemie International Edition, Environmental Science & Technology and Journal of Bacteriology.

In The Last Decade

Timothy E. H. Allen

25 papers receiving 650 citations

Peers

Countries citing papers authored by Timothy E. H. Allen

Since Specialization

Citations

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

Fields of papers citing papers by Timothy E. H. Allen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Timothy E. H. Allen. 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 Timothy E. H. Allen. The network helps show where Timothy E. H. Allen may publish in the future.

Co-authorship network of co-authors of Timothy E. H. Allen

This figure shows the co-authorship network connecting the top 25 collaborators of Timothy E. H. Allen. A scholar is included among the top collaborators of Timothy E. H. Allen 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 Timothy E. H. Allen. Timothy E. H. Allen 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

#	Work	Indexed citations
1	Machine Learning Informs RNA‐Binding Chemical Space** 2022 ·Angewandte Chemie International Edition ·(unknown), (unknown), Mo Yang, (unknown), David Calabrese, Robert E. Boer, Thomas A. Hilimire, Timothy E. H. Allen, (unknown), John S. Schneekloth	37
2	Machine Learning Informs RNA‐Binding Chemical Space** 2022 ·Angewandte Chemie ·(unknown), (unknown), Mo Yang, (unknown), David Calabrese, Robert E. Boer, Thomas A. Hilimire, Timothy E. H. Allen, (unknown), John S. Schneekloth	12
3	Towards quantifying the uncertainty in in silico predictions using Bayesian learning 2022 ·Computational Toxicology ·Timothy E. H. Allen, A. Middleton, Jonathan M. Goodman, Paul Russell, Predrag Kukić, Steve Gutsell	5
4	The role of ‘big data’ and ‘in silico’ New Approach Methodologies (NAMs) in ending animal use – A commentary on progress 2022 ·Computational Toxicology ·(unknown), Domenico Gadaleta, Timothy E. H. Allen	17
5	Use of chemical informatics, quantum chemistry modelling and artificial intelligence algorithms to predict molecular initiating events 2021 ·Toxicology Letters ·Timothy E. H. Allen, (unknown), Matthew N. Grayson, A. Middleton, (unknown), Maria Teresa Baltazar, (unknown), (unknown), Jonathan M. Goodman, Paul Russell, Predrag Kukić, Steve Gutsell	0
6	The use of adverse outcome pathways in the safety evaluation of food additives 2020 ·Archives of Toxicology ·Mathieu Vinken, Nynke I. Kramer, Timothy E. H. Allen, (unknown), (unknown), Sara Levorato, (unknown), Stefan Schulte, Alan R. Boobis, (unknown), Ivonne M.C.M. Rietjens	9
7	Confidence in Inactive and Active Predictions from Structural Alerts 2020 ·Chemical Research in Toxicology ·(unknown), Timothy E. H. Allen, Jonathan M. Goodman, Steve Gutsell, Predrag Kukić, Paul Russell	3
8	Neural network activation similarity: a new measure to assist decision making in chemical toxicology 2020 ·Chemical Science ·Timothy E. H. Allen, (unknown), (unknown), (unknown), Jonathan M. Goodman, Steve Gutsell, Paul Russell	16
9	In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays 2020 ·Environmental Science & Technology ·Timothy E. H. Allen, Mark Nelms, Stephen W. Edwards, Jonathan M. Goodman, Steve Gutsell, Paul Russell	8
10	Characterizing the coverage of critical effects relevant in the safety evaluation of food additives by AOPs 2019 ·Archives of Toxicology ·Nynke I. Kramer, (unknown), (unknown), (unknown), (unknown), Timothy E. H. Allen, Sara Levorato, (unknown), Stefan Schulte, Alan R. Boobis, Ivonne M.C.M. Rietjens, Mathieu Vinken	17
11	Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure–Activity Relationships 2019 ·Chemical Research in Toxicology ·Timothy E. H. Allen, Jonathan M. Goodman, Steve Gutsell, Paul Russell	14
12	Using Transition State Modeling To Predict Mutagenicity for Michael Acceptors 2018 ·Journal of Chemical Information and Modeling ·Timothy E. H. Allen, Matthew N. Grayson, Jonathan M. Goodman, Steve Gutsell, Paul Russell	12
13	The Forgotten Steps of Engineering Design: Design-Build Experiences and their Downstream Effect on Capstone Design Projects 2018 ·William H. Guilford, Timothy E. H. Allen, Shayn M. Peirce	1
14	A History of the Molecular Initiating Event 2016 ·Chemical Research in Toxicology ·Timothy E. H. Allen, Jonathan M. Goodman, Steve Gutsell, Paul Russell	44
15	Using Molecular Initiating Events To Generate 2D Structure–Activity Relationships for Toxicity Screening 2016 ·Chemical Research in Toxicology ·Timothy E. H. Allen, Sonia Liggi, Jonathan M. Goodman, Steve Gutsell, Paul Russell	15
16	Defining Molecular Initiating Events in the Adverse Outcome Pathway Framework for Risk Assessment 2014 ·Chemical Research in Toxicology ·Timothy E. H. Allen, Jonathan M. Goodman, Steve Gutsell, Paul Russell	116
17	Long-Range Periodic Patterns in Microbial Genomes Indicate Significant Multi-Scale Chromosomal Organization 2006 ·PLoS Computational Biology ·Timothy E. H. Allen, Nathan D. Price, Andrew R. Joyce, Bernhard Ø. Palsson	38
18	Long-range Periodic Patterns in Microbial Genomes Indicate Significant Multi-scale Chromosomal Organization 2005 ·PLoS Computational Biology ·Timothy E. H. Allen, Nathan D. Price, Andrew R. Joyce, Bernhard Ø. Palsson	5
19	Genome-Scale Analysis of the Uses of the Escherichia coli Genome: Model-Driven Analysis of Heterogeneous Data Sets 2003 ·Journal of Bacteriology ·Timothy E. H. Allen, Markus J. Herrgård, Mingzhu Liu, Yu Qiu, Jeremy D. Glasner, Frederick R. Blattner, Bernhard Ø. Palsson	63
20	Sequence-Based Analysis of Metabolic Demands for Protein Synthesis in Prokaryotes 2002 ·Journal of Theoretical Biology ·Timothy E. H. Allen, Bernhard Ø. Palsson	33

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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