Imran Shah

6.7k total citations · 2 hit papers
94 papers, 3.4k citations indexed

About

Imran Shah is a scholar working on Computational Theory and Mathematics, Molecular Biology and Small Animals. According to data from OpenAlex, Imran Shah has authored 94 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Computational Theory and Mathematics, 41 papers in Molecular Biology and 17 papers in Small Animals. Recurrent topics in Imran Shah's work include Computational Drug Discovery Methods (43 papers), Animal testing and alternatives (17 papers) and Bioinformatics and Genomic Networks (13 papers). Imran Shah is often cited by papers focused on Computational Drug Discovery Methods (43 papers), Animal testing and alternatives (17 papers) and Bioinformatics and Genomic Networks (13 papers). Imran Shah collaborates with scholars based in United States, Finland and Netherlands. Imran Shah's co-authors include Richard Judson, Grace Patlewicz, John F. Wambaugh, Ann M. Richard, Russell S. Thomas, Antony Williams, Kamel Mansouri, Keith A. Houck, Nancy Baker and Chris Grulke and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and Bioinformatics.

In The Last Decade

Imran Shah

88 papers receiving 3.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The CompTox Chemistry Dashboard: a community data resource for environmental chemistry

2017 789 citations Antony Williams, Chris Grulke et al. profile →
In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project

2009 435 citations Richard Judson, Keith A. Houck et al. Environmental Health Perspectives profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Imran Shah United States	28	1.2k	1.1k	1.0k	614	325	94	3.4k
Kamel Mansouri United States	25	1.5k 1.2×	1.7k 1.5×	1.1k 1.1×	485 0.8×	435 1.3×	52	4.0k
Daniel M. Rotroff United States	26	1.3k 1.1×	768 0.7×	910 0.9×	547 0.9×	163 0.5×	90	3.4k
Chris Grulke United States	26	1.7k 1.4×	1.0k 0.9×	948 0.9×	387 0.6×	549 1.7×	43	3.6k
Joanna Jaworska Poland	30	990 0.8×	2.0k 1.8×	1.0k 1.0×	518 0.8×	376 1.2×	76	4.7k
Warren Casey United States	30	821 0.7×	625 0.6×	994 1.0×	619 1.0×	133 0.4×	78	2.9k
Steven J. Enoch United Kingdom	31	720 0.6×	1.2k 1.0×	573 0.5×	401 0.7×	313 1.0×	79	2.7k
Sharon Munn Italy	17	1.1k 0.9×	416 0.4×	481 0.5×	365 0.6×	125 0.4×	24	2.3k
Stephen Ferguson United States	38	1.1k 0.9×	528 0.5×	1.6k 1.6×	460 0.7×	238 0.7×	108	5.1k
Alicia Paini Italy	25	780 0.6×	346 0.3×	388 0.4×	420 0.7×	169 0.5×	79	2.0k
Frédéric Y. Bois France	43	1.5k 1.2×	496 0.4×	1.6k 1.5×	321 0.5×	125 0.4×	217	6.0k

Countries citing papers authored by Imran Shah

Since Specialization

Citations

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

Fields of papers citing papers by Imran Shah

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Imran Shah

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Banerjee, Arkaprava, Supratik Kar, Kunal Roy, et al.. (2025). From Feature‐Based Chemical Similarity to Chemical Language Models—A Paradigm Shift in Computer‐Aided Molecular Design and Property Predictions. Wiley Interdisciplinary Reviews Computational Molecular Science. 15(6).

Patlewicz, Grace, et al.. (2025). Building a compendium of expert driven read-across cases to facilitate an analysis of the contribution that different similarity contexts play in read-across performance. Computational Toxicology. 35. 100366–100366. 1 indexed citations

Shah, Imran, et al.. (2025). Can graph similarity metrics be helpful for analogue identification as part of a read-across approach?. Computational Toxicology. 34. 100353–100353. 1 indexed citations

Shah, Imran, David A. Gallegos, Dennis J. Eastburn, et al.. (2025). Decoding cellular stress states for toxicology using single-cell transcriptomics. 1. 100046–100046.

Harrill, Joshua, Logan J. Everett, Derik E. Haggard, et al.. (2024). Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping. Toxicological Sciences. 202(1). 103–122. 10 indexed citations

Bundy, Joseph L., Logan J. Everett, Johanna Nyffeler, et al.. (2024). High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells. Toxicology and Applied Pharmacology. 491. 117073–117073. 6 indexed citations

Patlewicz, Grace, et al.. (2024). A systematic analysis of read-across within REACH registration dossiers. Computational Toxicology. 30. 100304–100304. 8 indexed citations

Franzosa, Jill A., Jessica A. Bonzo, John Jack, et al.. (2021). High-throughput toxicogenomic screening of chemicals in the environment using metabolically competent hepatic cell cultures. npj Systems Biology and Applications. 7(1). 7–7. 27 indexed citations

Lee, Fan, et al.. (2021). Reproducibility and robustness of high-throughput S1500+ transcriptomics on primary rat hepatocytes for chemical-induced hepatotoxicity assessment. Current Research in Toxicology. 2. 282–295. 4 indexed citations

10.

Shah, Imran, et al.. (2021). Evaluating adaptive stress response gene signatures using transcriptomics. Computational Toxicology. 20. 100179–100179. 7 indexed citations

11.

Williams, Antony, Chris Grulke, Ann M. Richard, et al.. (2019). US-EPA CompTox Chemicals Dashboard – integrating chemistry and biology data to serve computational toxicology and environmental science. Figshare. 1 indexed citations

12.

Huang, Xiaozhong, Fan Lee, 要子伊藤, et al.. (2019). Sequential drug delivery for liver diseases. Advanced Drug Delivery Reviews. 149-150. 72–84. 7 indexed citations

13.

Harrill, Joshua, Imran Shah, R. Woodrow Setzer, et al.. (2019). Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulatory decisions. Current Opinion in Toxicology. 15. 64–75. 69 indexed citations

14.

Lee, Jaeyoung, Mohamed Abdel‐Aty, & Imran Shah. (2018). Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling. Accident Analysis & Prevention. 130. 91–98. 55 indexed citations

15.

Shah, Imran, et al.. (2017). Evaluation of Surrogate Measures for Pedestrian Exposure to Traffic Crashes at Intersections. Transportation Research Board 96th Annual MeetingTransportation Research Board. 1 indexed citations

16.

Shah, Imran, Jie Liu, Richard Judson, Russell S. Thomas, & Grace Patlewicz. (2016). Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information. Regulatory Toxicology and Pharmacology. 79. 12–24. 67 indexed citations

17.

Shah, Imran, R. Woodrow Setzer, John Jack, et al.. (2015). Using ToxCast™ Data to Reconstruct Dynamic Cell State Trajectories and Estimate Toxicological Points of Departure. Environmental Health Perspectives. 124(7). 910–919. 58 indexed citations

18.

Judson, Richard, Holly M. Mortensen, Imran Shah, Thomas B. Knudsen, & Fathi Elloumi. (2011). Using pathway modules as targets for assay development in xenobiotic screening. Molecular BioSystems. 8(2). 531–542. 13 indexed citations

19.

Judson, Richard, Fathi Elloumi, R. Woodrow Setzer, Zhen Li, & Imran Shah. (2008). A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinformatics. 9(1). 241–241. 57 indexed citations

20.

Rao, Shaoqi, et al.. (2003). PathMiner: predicting metabolic pathways by heuristic search. Bioinformatics. 19(13). 1692–1698. 55 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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