Deepti Malhotra

2.8k total citations · 1 hit paper
52 papers, 1.7k citations indexed

About

Deepti Malhotra is a scholar working on Molecular Biology, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Deepti Malhotra has authored 52 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 13 papers in Computer Networks and Communications and 10 papers in Information Systems. Recurrent topics in Deepti Malhotra's work include Cloud Computing and Resource Management (9 papers), Genomics, phytochemicals, and oxidative stress (7 papers) and IoT and Edge/Fog Computing (7 papers). Deepti Malhotra is often cited by papers focused on Cloud Computing and Resource Management (9 papers), Genomics, phytochemicals, and oxidative stress (7 papers) and IoT and Edge/Fog Computing (7 papers). Deepti Malhotra collaborates with scholars based in India, United States and Canada. Deepti Malhotra's co-authors include Shyam Biswal, Thomas W. Kensler, Anju Singh, Christine Happel, David J. Arenillas, Wyeth W. Wasserman, Élodie Portales-Casamar, Casper Shyr, Nobunao Wakabayashi and Siddhartha Srivastava and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Medicine.

In The Last Decade

Deepti Malhotra

48 papers receiving 1.6k citations

Hit Papers

Global mapping of binding sites for Nrf2 identifies novel... 2010 2026 2015 2020 2010 200 400 600

Peers

Deepti Malhotra
Alexandria Lau United States
Lei Yu China
Jiyoung Kim South Korea
Xiaoli Ru China
Sang Joon Lee South Korea
Deepti Malhotra
Citations per year, relative to Deepti Malhotra Deepti Malhotra (= 1×) peers Hiromi Sato

Countries citing papers authored by Deepti Malhotra

Since Specialization
Citations

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

Fields of papers citing papers by Deepti Malhotra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deepti Malhotra

This figure shows the co-authorship network connecting the top 25 collaborators of Deepti Malhotra. A scholar is included among the top collaborators of Deepti Malhotra 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 Deepti Malhotra. Deepti Malhotra 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.
Singh, Archana, et al.. (2025). ESG Implementation in Indian NIFTY 500 Companies: A Comprehensive Multi-case Analysis of Strategy, Implementation, and Value Creation. South Asian Journal of Business and Management Cases. 14(3). 243–260.
2.
Malhotra, Deepti, et al.. (2025). Ensembler_fMRI: An Intelligent Approach for the Early Prediction of Autism Disorder. Procedia Computer Science. 259. 1863–1873.
3.
Malhotra, Deepti, et al.. (2024). SSMDA: Semi-supervised multi-source domain adaptive autism prediction model using neuroimaging. Biomedical Signal Processing and Control. 95. 106337–106337. 1 indexed citations
4.
Malhotra, Deepti, et al.. (2024). USMDA: Unsupervised Multisource Domain Adaptive ADHD prediction model using neuroimaging. Knowledge-Based Systems. 305. 112615–112615. 1 indexed citations
5.
Malhotra, Deepti, et al.. (2023). Prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review. Lecture notes in electrical engineering. 181–198. 4 indexed citations
6.
Malhotra, Deepti, et al.. (2023). Hybrid Deep Learning Model for COVID-19 Prediction Using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (LSTM) Network. International Journal of Computer Theory and Engineering. 15(3). 125–129. 3 indexed citations
7.
Malhotra, Deepti, et al.. (2023). A Survey on the role of ML and AI in fighting Covid-19. 7. 27–32. 1 indexed citations
8.
Malhotra, Deepti, et al.. (2023). A systematic study of intelligent autism spectrum disorder detector. International Journal of Computational Vision and Robotics. 13(2). 219–219. 3 indexed citations
9.
Malhotra, Deepti, et al.. (2022). Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation. Archives of Computational Methods in Engineering. 30(3). 1789–1818. 27 indexed citations
10.
Malhotra, Deepti, et al.. (2022). Correction to: Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation. Archives of Computational Methods in Engineering. 30(5). 3485–3485. 1 indexed citations
11.
Malhotra, Deepti, et al.. (2021). An adaptive threshold policy for host overload detection in cloud data centre. International Journal of Advanced Technology and Engineering Exploration. 8(83). 1 indexed citations
12.
Regard, Jean B., Deepti Malhotra, Jelena Gvozdenovic‐Jeremic, et al.. (2013). Activation of Hedgehog signaling by loss of GNAS causes heterotopic ossification. Nature Medicine. 19(11). 1505–1512. 172 indexed citations
13.
McGrath‐Morrow, Sharon A., Thomas Lauer, Joseph M. Collaco, et al.. (2013). Transcriptional responses of neonatal mouse lung to hyperoxia by Nrf2 status. Cytokine. 65(1). 4–9. 37 indexed citations
14.
McGrath‐Morrow, Sharon A., Deepti Malhotra, Thomas Lauer, et al.. (2011). Exposure to neonatal cigarette smoke causes durable lung changes but does not potentiate cigarette smoke–induced chronic obstructive pulmonary disease in adult mice. Experimental Lung Research. 37(6). 354–363. 1 indexed citations
15.
Malhotra, Deepti, et al.. (2011). Protective Role of Ascorbic Acid (Vitamin C) Against Hyperlipidemia and Enhanced Oxidizability of Low Density Lipoprotein in Young Smokers. European Journal of Experimental Biology. 1(1). 2 indexed citations
16.
Rawat, Seema, et al.. (2011). Hypolipidemic effects and Antioxidant Activity of Tocotrienols on CigaretteSmoke Exposed Rats. Der Chemica Sinica. 2(2). 1 indexed citations
17.
Malhotra, Deepti, Élodie Portales-Casamar, Anju Singh, et al.. (2010). Global mapping of binding sites for Nrf2 identifies novel targets in cell survival response through ChIP-Seq profiling and network analysis. Nucleic Acids Research. 38(17). 5718–5734. 632 indexed citations breakdown →
18.
Blake, David J., Anju Singh, Ponvijay Kombairaju, et al.. (2009). Deletion of Keap1 in the Lung Attenuates Acute Cigarette Smoke–Induced Oxidative Stress and Inflammation. American Journal of Respiratory Cell and Molecular Biology. 42(5). 524–536. 123 indexed citations
19.
Sussan, Thomas E., Tirumalai Rangasamy, David J. Blake, et al.. (2008). Targeting Nrf2 with the triterpenoid CDDO- imidazolide attenuates cigarette smoke-induced emphysema and cardiac dysfunction in mice. Proceedings of the National Academy of Sciences. 106(1). 250–255. 282 indexed citations
20.
Taylor, Ronald C., George K. Acquaah-Mensah, Mudita Singhal, Deepti Malhotra, & Shyam Biswal. (2008). Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress. PLoS Computational Biology. 4(8). e1000166–e1000166. 76 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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