Animesh Acharjee

2.8k total citations
96 papers, 1.7k citations indexed

About

Animesh Acharjee is a scholar working on Molecular Biology, Epidemiology and Oncology. According to data from OpenAlex, Animesh Acharjee has authored 96 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 22 papers in Epidemiology and 16 papers in Oncology. Recurrent topics in Animesh Acharjee's work include Metabolomics and Mass Spectrometry Studies (15 papers), Gut microbiota and health (13 papers) and Bioinformatics and Genomic Networks (13 papers). Animesh Acharjee is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (15 papers), Gut microbiota and health (13 papers) and Bioinformatics and Genomic Networks (13 papers). Animesh Acharjee collaborates with scholars based in United Kingdom, United States and India. Animesh Acharjee's co-authors include Georgios V. Gkoutos, Richard G. F. Visser, Chris Maliepaard, Julian L. Griffin, Bjorn Kloosterman, Zsuzsanna Ament, Mohammed Nabil Quraishi, C. Bachem, Yuanwei Xu and Andrew D. Beggs and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Animesh Acharjee

86 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Animesh Acharjee United Kingdom 25 722 269 224 188 179 96 1.7k
Guohai Su China 24 607 0.8× 187 0.7× 445 2.0× 158 0.8× 101 0.6× 97 1.9k
Hongyu Wu China 26 935 1.3× 134 0.5× 201 0.9× 136 0.7× 208 1.2× 97 2.1k
Yuemei Zhang China 24 612 0.8× 136 0.5× 124 0.6× 287 1.5× 171 1.0× 78 1.8k
Ji Yeon Seo South Korea 29 830 1.1× 174 0.6× 108 0.5× 206 1.1× 353 2.0× 117 2.3k
Gareth J. McKay United Kingdom 27 1.0k 1.4× 213 0.8× 152 0.7× 240 1.3× 69 0.4× 145 3.2k
Xiaoling Cai China 27 697 1.0× 309 1.1× 200 0.9× 122 0.6× 189 1.1× 185 2.7k
Liang Leng China 17 462 0.6× 170 0.6× 124 0.6× 157 0.8× 199 1.1× 45 1.3k
Shangjie Wu China 16 666 0.9× 169 0.6× 88 0.4× 190 1.0× 121 0.7× 56 1.8k
Jinhui Zhang China 20 430 0.6× 233 0.9× 98 0.4× 96 0.5× 146 0.8× 95 1.6k

Countries citing papers authored by Animesh Acharjee

Since Specialization
Citations

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

Fields of papers citing papers by Animesh Acharjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Animesh Acharjee

This figure shows the co-authorship network connecting the top 25 collaborators of Animesh Acharjee. A scholar is included among the top collaborators of Animesh Acharjee 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 Animesh Acharjee. Animesh Acharjee 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.
Radhakrishnan, S, et al.. (2025). Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data. Frontiers in Oncology. 14. 1505675–1505675. 5 indexed citations
2.
Acharjee, Animesh, et al.. (2024). Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study. SHILAP Revista de lepidopterología. 4(1). 12–12. 3 indexed citations
3.
Gkoutos, Georgios V., et al.. (2024). Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations. iScience. 27(7). 110298–110298. 10 indexed citations
4.
Sadozai, Hassan, et al.. (2024). High hypoxia status in pancreatic cancer is associated with multiple hallmarks of an immunosuppressive tumor microenvironment. Frontiers in Immunology. 15. 1360629–1360629. 13 indexed citations
5.
Acharjee, Animesh, et al.. (2024). Unravelling metabolite-microbiome interactions in inflammatory bowel disease through AI and interaction-based modelling. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1871(3). 167618–167618. 7 indexed citations
6.
Gkoutos, Georgios V., et al.. (2024). Analysis of translesion polymerases in colorectal cancer cells following cetuximab treatment: A network perspective. Cancer Medicine. 13(1). e6945–e6945. 2 indexed citations
7.
Mi, Ningning, Man Yang, Lina Wei, et al.. (2024). Gallstone Disease Is Associated With an Increased Risk of Inflammatory Bowel Disease: Results From 3 Prospective Cohort Studies. The American Journal of Gastroenterology. 120(1). 204–212.
8.
Acharjee, Animesh, et al.. (2022). The diagnostic potential and barriers of microbiome based therapeutics. Diagnosis. 9(4). 411–420. 11 indexed citations
9.
Williams, John A., et al.. (2022). Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models. PLoS ONE. 17(2). e0263390–e0263390. 5 indexed citations
10.
Xu, Yuanwei, et al.. (2021). Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer. International Journal of Molecular Sciences. 22(11). 5763–5763. 21 indexed citations
11.
Sadozai, Hassan, Animesh Acharjee, Thomas Gruber, Beat Gloor, & Eva Karamitopoulou. (2021). Pancreatic Cancers with High Grade Tumor Budding Exhibit Hallmarks of Diminished Anti-Tumor Immunity. Cancers. 13(5). 1090–1090. 9 indexed citations
12.
Horniblow, Richard D., Dario Leonardo Balacco, Animesh Acharjee, et al.. (2021). Iron-mediated epigenetic activation of NRF2 targets. The Journal of Nutritional Biochemistry. 101. 108929–108929. 27 indexed citations
13.
Gkoutos, Georgios V., et al.. (2021). Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers. Biomedicines. 9(11). 1636–1636. 8 indexed citations
14.
Williams, John A., et al.. (2021). A Causal Web between Chronotype and Metabolic Health Traits. Genes. 12(7). 1029–1029. 2 indexed citations
15.
Mena, Pedro, Claudia Favari, Animesh Acharjee, et al.. (2021). Metabotypes of flavan-3-ol colonic metabolites after cranberry intake: elucidation and statistical approaches. European Journal of Nutrition. 61(3). 1299–1317. 21 indexed citations
16.
Acharjee, Animesh, Jon Hazeldine, Alina Bazarova, et al.. (2021). Integration of Metabolomic and Clinical Data Improves the Prediction of Intensive Care Unit Length of Stay Following Major Traumatic Injury. Metabolites. 12(1). 29–29. 10 indexed citations
17.
18.
Liu, Kedi, Animesh Acharjee, Christine Hinz, et al.. (2020). Consequences of Lipid Remodeling of Adipocyte Membranes Being Functionally Distinct from Lipid Storage in Obesity. Journal of Proteome Research. 19(10). 3919–3935. 12 indexed citations
19.
Acharjee, Animesh, Jon Hazeldine, Conor Bentley, et al.. (2019). Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction. Scientific Data. 6(1). 328–328. 18 indexed citations
20.
Acharjee, Animesh. (2012). Comparison of Regularized Regression Methods for ~Omics Data. Socio-Environmental Systems Modeling. 3(3). 37 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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