Arpit Tandon

1.4k total citations
19 papers, 701 citations indexed

About

Arpit Tandon is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Arpit Tandon has authored 19 papers receiving a total of 701 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 3 papers in Oncology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Arpit Tandon's work include Computational Drug Discovery Methods (3 papers), RNA modifications and cancer (3 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Arpit Tandon is often cited by papers focused on Computational Drug Discovery Methods (3 papers), RNA modifications and cancer (3 papers) and Metabolomics and Mass Spectrometry Studies (2 papers). Arpit Tandon collaborates with scholars based in United States, India and Netherlands. Arpit Tandon's co-authors include Ruchir Shah, Jason Phillips, Deepak Mav, Brian E. Howard, Andrew A. Rooney, B. Alex Merrick, Kristina A. Thayer, Vickie R. Walker, Siddharth Sinha and Cristian Coarfa and has published in prestigious journals such as Nucleic Acids Research, Environmental Science & Technology and Bioinformatics.

In The Last Decade

Arpit Tandon

19 papers receiving 692 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Arpit Tandon United States	15	297	147	111	71	70	19	701
Jason Phillips United States	9	143 0.5×	162 1.1×	54 0.5×	77 1.1×	82 1.2×	26	586
Brian E. Howard United States	10	194 0.7×	74 0.5×	35 0.3×	35 0.5×	25 0.4×	16	567
Deepak Mav United States	18	1.1k 3.5×	278 1.9×	358 3.2×	85 1.2×	56 0.8×	35	1.8k
Yi‐Hui Zhou United States	16	458 1.5×	82 0.6×	78 0.7×	37 0.5×	30 0.4×	51	867
Cameron MacKay United Kingdom	12	92 0.3×	152 1.0×	35 0.3×	140 2.0×	141 2.0×	23	592
Sylvia E. Escher Germany	19	228 0.8×	271 1.8×	117 1.1×	154 2.2×	183 2.6×	81	1.1k
Ilona Silins Sweden	12	280 0.9×	148 1.0×	112 1.0×	35 0.5×	8 0.1×	28	624
Clemens Wittwehr Italy	9	83 0.3×	89 0.6×	22 0.2×	115 1.6×	65 0.9×	20	329
Shannon Bell United States	13	206 0.7×	136 0.9×	41 0.4×	133 1.9×	114 1.6×	21	531
David S. Salsburg United States	14	117 0.4×	78 0.5×	165 1.5×	19 0.3×	23 0.3×	46	721

Countries citing papers authored by Arpit Tandon

Since Specialization

Citations

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

Fields of papers citing papers by Arpit Tandon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arpit Tandon

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

All Works

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19 of 19 papers shown

Tandon, Arpit, Brian E. Howard, Adrian J. Green, et al.. (2025). Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening. Aquatic Toxicology. 285. 107415–107415. 1 indexed citations

Ramaiahgari, Sreenivasa, Scott S. Auerbach, Georgia Roberts, et al.. (2025). Unraveling Human Hepatocellular Responses to PFAS and Aqueous Film-Forming Foams (AFFFs) for Molecular Hazard Prioritization and In Vivo Translation. Environmental Science & Technology. 59(5). 2423–2435. 4 indexed citations

Tandon, Arpit, Brian E. Howard, Sreenivasa Ramaiahgari, et al.. (2022). Deep Learning Image Analysis of High-Throughput Toxicology Assay Images. SLAS DISCOVERY. 27(1). 29–38. 9 indexed citations

Schmidt, Lena, Juleen Lam, Brian E. Howard, et al.. (2020). Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map. Frontiers in Public Health. 8. 582205–582205. 18 indexed citations

Howard, Brian E., Jason Phillips, Arpit Tandon, et al.. (2020). SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation. Environment International. 138. 105623–105623. 90 indexed citations

Bell, Shannon, Patricia Ceger, Xiaoqing Chang, et al.. (2020). An integrated chemical environment with tools for chemical safety testing. Toxicology in Vitro. 67. 104916–104916. 45 indexed citations

Phillips, Jason, Daniel Svoboda, Arpit Tandon, et al.. (2018). BMDExpress 2: enhanced transcriptomic dose-response analysis workflow. Bioinformatics. 35(10). 1780–1782. 153 indexed citations

Williams, Benfeard, Bo Zhao, Arpit Tandon, et al.. (2017). Structure modeling of RNA using sparse NMR constraints. Nucleic Acids Research. 45(22). 12638–12647. 17 indexed citations

Thompson, Peter M., Srinivas Ramachandran, Lindsay B. Case, et al.. (2017). A Structural Model for Vinculin Insertion into PIP2-Containing Membranes and the Effect of Insertion on Vinculin Activation and Localization. Structure. 25(2). 264–275. 22 indexed citations

10.

Bell, Shannon, Jason Phillips, Alexander Sedykh, et al.. (2017). An Integrated Chemical Environment to Support 21st-Century Toxicology. Environmental Health Perspectives. 125(5). 54501–54501. 41 indexed citations

11.

Singh, Ajeet P., Julie F. Foley, Arpit Tandon, et al.. (2017). A role for BRG1 in the regulation of genes required for development of the lymphatic system. Oncotarget. 8(33). 54925–54938. 6 indexed citations

12.

Howard, Brian E., Jason Phillips, Kyle J. Miller, et al.. (2016). SWIFT-Review: a text-mining workbench for systematic review. Systematic Reviews. 5(1). 87–87. 120 indexed citations

13.

Singh, Ajeet P., Julie F. Foley, Mark Rubino, et al.. (2016). Brg1 Enables Rapid Growth of the Early Embryo by Suppressing Genes That Regulate Apoptosis and Cell Growth Arrest. Molecular and Cellular Biology. 36(15). 1990–2010. 30 indexed citations

14.

Homan, Philip, Arpit Tandon, Greggory M. Rice, et al.. (2014). RNA Tertiary Structure Analysis by 2′-Hydroxyl Molecular Interference. Biochemistry. 53(43). 6825–6833. 14 indexed citations

15.

Coarfa, Cristian, Andrew P. Jackson, Arpit Tandon, et al.. (2014). Analysis of interactions between the epigenome and structural mutability of the genome using Genboree workbench tools. BMC Bioinformatics. 15(S7). S2–S2. 17 indexed citations

16.

Smith, Lindsay K, Arpit Tandon, Ruchir Shah, et al.. (2013). Deep Sequencing Identification of Novel Glucocorticoid-Responsive miRNAs in Apoptotic Primary Lymphocytes. PLoS ONE. 8(10). e78316–e78316. 14 indexed citations

17.

Riehle, Kevin, Cristian Coarfa, Andrew P. Jackson, et al.. (2012). The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences. BMC Bioinformatics. 13(S13). S11–S11. 44 indexed citations

18.

Gunaratne, Preethi H., Cristian Coarfa, Benjamin Soibam, & Arpit Tandon. (2011). miRNA Data Analysis: Next-Gen Sequencing. Methods in molecular biology. 822. 273–288. 25 indexed citations

19.

Tandon, Arpit & Siddharth Sinha. (2011). Structural insights into the binding of MMP9 inhibitors. Bioinformation. 5(8). 310–314. 31 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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