Mohanapriya Arumugam

624 total citations
39 papers, 421 citations indexed

About

Mohanapriya Arumugam is a scholar working on Molecular Biology, Immunology and Computational Theory and Mathematics. According to data from OpenAlex, Mohanapriya Arumugam has authored 39 papers receiving a total of 421 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 12 papers in Immunology and 7 papers in Computational Theory and Mathematics. Recurrent topics in Mohanapriya Arumugam's work include Computational Drug Discovery Methods (7 papers), Psoriasis: Treatment and Pathogenesis (6 papers) and Protein Structure and Dynamics (3 papers). Mohanapriya Arumugam is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Psoriasis: Treatment and Pathogenesis (6 papers) and Protein Structure and Dynamics (3 papers). Mohanapriya Arumugam collaborates with scholars based in India, Japan and Saudi Arabia. Mohanapriya Arumugam's co-authors include Sajitha Lulu, Pandjassarame Kangueane, R. Sudhakaran, Anshul Sukhwal, Toshiaki Itami, S. L. Manju, Vino Sundararajan, M. A. Jayasri, T. V. Sravanthi and Jeyaraman Jeyakanthan and has published in prestigious journals such as PLoS ONE, Gene and RSC Advances.

In The Last Decade

Mohanapriya Arumugam

36 papers receiving 399 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mohanapriya Arumugam India 13 158 93 77 47 43 39 421
Chien-Fu Huang Taiwan 12 225 1.4× 62 0.7× 32 0.4× 44 0.9× 29 0.7× 22 495
Yongping Zhu China 13 247 1.6× 66 0.7× 96 1.2× 42 0.9× 65 1.5× 36 594
Xingkang Wu China 15 257 1.6× 51 0.5× 54 0.7× 31 0.7× 37 0.9× 40 444
Jung-Keun Suh South Korea 14 255 1.6× 106 1.1× 53 0.7× 58 1.2× 39 0.9× 35 688
Chinmoy Banerjee India 11 214 1.4× 67 0.7× 60 0.8× 30 0.6× 28 0.7× 18 462
Hongfei Chen China 14 210 1.3× 57 0.6× 135 1.8× 20 0.4× 17 0.4× 37 429
Ruirui Yang China 13 243 1.5× 89 1.0× 35 0.5× 100 2.1× 120 2.8× 36 493
Hongyan Guo China 12 171 1.1× 143 1.5× 90 1.2× 19 0.4× 24 0.6× 28 510
Konrad Diedrich Germany 5 219 1.4× 32 0.3× 59 0.8× 91 1.9× 19 0.4× 7 376
Inés Maestro Spain 10 234 1.5× 42 0.5× 82 1.1× 170 3.6× 18 0.4× 11 613

Countries citing papers authored by Mohanapriya Arumugam

Since Specialization
Citations

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

Fields of papers citing papers by Mohanapriya Arumugam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohanapriya Arumugam

This figure shows the co-authorship network connecting the top 25 collaborators of Mohanapriya Arumugam. A scholar is included among the top collaborators of Mohanapriya Arumugam 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 Mohanapriya Arumugam. Mohanapriya Arumugam 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.
Arumugam, Mohanapriya, et al.. (2025). Transcriptomic analysis reveals potential biomarkers for early-onset pre-eclampsia using integrative bioinformatics and LASSO based approach. Computers in Biology and Medicine. 192(Pt B). 110203–110203.
2.
Arumugam, Mohanapriya, et al.. (2024). Pre-eclampsia: Re-visiting pathophysiology, role of immune cells, biomarker identification and recent advances in its management. Journal of Reproductive Immunology. 163. 104236–104236.
3.
Choudhury, Abbas Alam, Mohanapriya Arumugam, D. Sivaraman, et al.. (2024). Anti-diabetic drug discovery using the bioactive compounds of Momordica charantia by molecular docking and molecular dynamics analysis. Journal of Biomolecular Structure and Dynamics. 43(15). 8370–8384. 8 indexed citations
4.
Ahmad, Faraz, et al.. (2024). The multifaceted functions of long non-coding RNAHOTAIRin neuropathologies and its potential as a prognostic marker and therapeutic biotarget. Expert Reviews in Molecular Medicine. 26. e11–e11. 4 indexed citations
5.
Arumugam, Mohanapriya, et al.. (2024). Thyroid Cancer Prediction Using Deep Learning Techniques. 159–164.
6.
Arumugam, Mohanapriya, et al.. (2024). Unveiling therapeutic biomarkers and druggable targets in ALS: An integrative microarray analysis, molecular docking, and structural dynamic studies. Computational Biology and Chemistry. 113. 108211–108211. 1 indexed citations
7.
Vashishth, Rahul, et al.. (2023). Neuroprotection by agmatine: Possible involvement of the gut microbiome?. Ageing Research Reviews. 91. 102056–102056. 15 indexed citations
8.
Arumugam, Mohanapriya, et al.. (2023). Computational investigation of phytochemicals identified from medicinal plant extracts against tuberculosis. Journal of Biomolecular Structure and Dynamics. 42(7). 3382–3395. 4 indexed citations
9.
Arumugam, Mohanapriya, et al.. (2021). Interaction of Host Pattern Recognition Receptors (PRRs) withMycobacterium Tuberculosisand Ayurvedic Management of Tuberculosis: A Systemic Approach. Infectious Disorders - Drug Targets. 22(2). e130921196420–e130921196420. 3 indexed citations
10.
Jeyakanthan, Jeyaraman, et al.. (2020). Exploring genetic targets of psoriasis using genome wide association studies (GWAS) for drug repurposing. 3 Biotech. 10(2). 43–43. 11 indexed citations
11.
Arumugam, Mohanapriya, et al.. (2020). Next generation sequencing exome data analysis aids in the discovery of SNP and INDEL patterns in Parkinson's disease. Genomics. 112(5). 3722–3728. 9 indexed citations
12.
13.
Madhyastha, Harishkumar, Suman Halder, Radha Madhyastha, et al.. (2020). Surface refined AuQuercetinnanoconjugate stimulates dermal cell migration: possible implication in wound healing. RSC Advances. 10(62). 37683–37694. 22 indexed citations
14.
Arumugam, Mohanapriya, et al.. (2020). Multiple Gene Expression Dataset Analysis Reveals Toll-Like Receptor Signaling Pathway is Strongly Associated With Chronic Obstructive Pulmonary Disease Pathogenesis. COPD Journal of Chronic Obstructive Pulmonary Disease. 17(6). 684–698. 3 indexed citations
15.
Lulu, Sajitha, et al.. (2017). Deciphering the Mechanism of Action of Wrightia tinctoria for Psoriasis Based on Systems Pharmacology Approach. The Journal of Alternative and Complementary Medicine. 23(11). 866–878. 5 indexed citations
16.
Arumugam, Mohanapriya, et al.. (2017). A systems pharmacology perspective to decipher the mechanism of action of Parangichakkai chooranam, a Siddha formulation for the treatment of psoriasis. Biomedicine & Pharmacotherapy. 88. 74–86. 16 indexed citations
17.
Sukhwal, Anshul, et al.. (2017). Protein-protein interfaces are vdW dominant with selective H-bonds and (or) electrostatics towards broad functional specificity. Bioinformation. 13(6). 164–173. 30 indexed citations
18.
Arumugam, Mohanapriya, et al.. (2010). A HLA-DRB supertype chart with potential overlapping peptide binding function. Bioinformation. 4(7). 300–309. 2 indexed citations
19.
Arumugam, Mohanapriya, et al.. (2009). Class II HLA-peptide binding prediction using structural principles. Human Immunology. 70(3). 159–169. 7 indexed citations
20.
Lulu, Sajitha, et al.. (2009). Structural features for homodimer folding mechanism. Journal of Molecular Graphics and Modelling. 28(2). 88–94. 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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