Malabika Datta

1.9k total citations
43 papers, 1.5k citations indexed

About

Malabika Datta is a scholar working on Molecular Biology, Cancer Research and Cell Biology. According to data from OpenAlex, Malabika Datta has authored 43 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 18 papers in Cancer Research and 9 papers in Cell Biology. Recurrent topics in Malabika Datta's work include Cancer-related molecular mechanisms research (14 papers), MicroRNA in disease regulation (12 papers) and RNA modifications and cancer (12 papers). Malabika Datta is often cited by papers focused on Cancer-related molecular mechanisms research (14 papers), MicroRNA in disease regulation (12 papers) and RNA modifications and cancer (12 papers). Malabika Datta collaborates with scholars based in India, Switzerland and Israel. Malabika Datta's co-authors include Gaurav Verma, Amit Kumar Pandey, Priyanka Agarwal, Rahul Srivastava, Himanshi Bhatia, Kirandeep Kaur, S Vig, Neha Goyal, Arvind K. Srivastava and Swayam Prakash Srivastava and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and PLoS ONE.

In The Last Decade

Malabika Datta

40 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Malabika Datta India 23 1.0k 706 206 150 144 43 1.5k
Sadeesh K. Ramakrishnan United States 20 710 0.7× 435 0.6× 301 1.5× 153 1.0× 196 1.4× 38 1.6k
Hua‐Fu Zhou China 11 951 0.9× 358 0.5× 116 0.6× 116 0.8× 104 0.7× 54 1.7k
Zhaoyuan Hou China 27 1.6k 1.6× 556 0.8× 246 1.2× 151 1.0× 215 1.5× 58 2.3k
Jian Zhao China 26 1.2k 1.1× 742 1.1× 254 1.2× 161 1.1× 58 0.4× 98 2.0k
Wei Shen China 19 678 0.7× 274 0.4× 196 1.0× 103 0.7× 222 1.5× 57 1.4k
Hanmei Xu China 19 889 0.9× 479 0.7× 160 0.8× 92 0.6× 45 0.3× 40 1.4k
Masayuki Shiota Japan 25 918 0.9× 332 0.5× 118 0.6× 163 1.1× 106 0.7× 64 1.6k
Yi Xie China 21 759 0.7× 279 0.4× 143 0.7× 126 0.8× 88 0.6× 59 1.3k
Ying Du China 22 746 0.7× 294 0.4× 307 1.5× 234 1.6× 225 1.6× 50 1.5k

Countries citing papers authored by Malabika Datta

Since Specialization
Citations

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

Fields of papers citing papers by Malabika Datta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Malabika Datta

This figure shows the co-authorship network connecting the top 25 collaborators of Malabika Datta. A scholar is included among the top collaborators of Malabika Datta 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 Malabika Datta. Malabika Datta 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.
Hazra, Susanta, et al.. (2025). Dicer inhibition delays wound closure by increasing Suz12 levels and regulating ITGAV levels in keratinocytes. Journal of Molecular Endocrinology. 74(3).
2.
Imai, Daisuke, Malabika Datta, Scot A. Wolfe, et al.. (2025). Reassessment of Biomarkers and Acceptance Criteria in Normothermic Machine Perfusion for Liver Allografts. American Journal of Transplantation. 25(8). S712–S712.
3.
4.
Verma, Amit Kumar, et al.. (2023). mir-98-5p regulates gluconeogenesis and lipogenesis by targeting PPP1R15B in hepatocytes. Journal of Cell Communication and Signaling. 17(3). 881–895. 6 indexed citations
5.
Singh, Vijay Pal, et al.. (2021). Correction to: Downregulation of miRNAs during delayed wound healing in diabetes: role of Dicer. Molecular Medicine. 27(1). 87–87. 1 indexed citations
6.
Tiwary, Shweta, et al.. (2021). GRP75 mediates endoplasmic reticulum–mitochondria coupling during palmitate-induced pancreatic β-cell apoptosis. Journal of Biological Chemistry. 297(6). 101368–101368. 32 indexed citations
7.
Datta, Malabika, et al.. (2020). LncRNAs in cancer: Regulatory and therapeutic implications. Cancer Letters. 501. 162–171. 122 indexed citations
8.
Goyal, Neha, et al.. (2018). Lnc-ing non-coding RNAs with metabolism and diabetes: roles of lncRNAs. Cellular and Molecular Life Sciences. 75(10). 1827–1837. 63 indexed citations
9.
Goyal, Neha, Ambily Sivadas, Shamsudheen Karuthedath Vellarikkal, et al.. (2017). RNA sequencing of db/db mice liver identifies lncRNA H19 as a key regulator of gluconeogenesis and hepatic glucose output. Scientific Reports. 7(1). 8312–8312. 43 indexed citations
10.
Bhatia, Himanshi, Bijay Pattnaik, & Malabika Datta. (2015). Inhibition of mitochondrial β-oxidation by miR-107 promotes hepatic lipid accumulation and impairs glucose tolerance in vivo. International Journal of Obesity. 40(5). 861–869. 31 indexed citations
11.
Rajagopal, Raman, et al.. (2014). Role of Calmodulin-Calmodulin Kinase II, cAMP/Protein Kinase A and ERK 1/2 on Aeromonas hydrophila-Induced Apoptosis of Head Kidney Macrophages. PLoS Pathogens. 10(4). e1004018–e1004018. 34 indexed citations
12.
Agarwal, Priyanka, Arvind K. Srivastava, Rahul Srivastava, Shakir Ali, & Malabika Datta. (2013). miR-135a targets IRS2 and regulates insulin signaling and glucose uptake in the diabetic gastrocnemius skeletal muscle. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1832(8). 1294–1303. 70 indexed citations
13.
Verma, Gaurav, Himanshi Bhatia, & Malabika Datta. (2013). JNK1/2 regulates ER–mitochondrial Ca2+ cross-talk during IL-1β–mediated cell death in RINm5F and human primary β-cells. Molecular Biology of the Cell. 24(12). 2058–2071. 26 indexed citations
14.
Jain, Priyanka, et al.. (2013). Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes. PLoS ONE. 8(1). e53522–e53522. 49 indexed citations
15.
Goswami, Ramansu, et al.. (2012). Aeromonas hydrophila induced head kidney macrophage apoptosis in Clarias batrachus involves the activation of calpain and is caspase-3 mediated. Developmental & Comparative Immunology. 37(3-4). 323–333. 39 indexed citations
16.
Verma, Gaurav, Himanshi Bhatia, & Malabika Datta. (2012). Gene expression profiling and pathway analysis identify the integrin signaling pathway to be altered by IL-1β in human pancreatic cancer cells: Role of JNK. Cancer Letters. 320(1). 86–95. 22 indexed citations
17.
Kaur, Kirandeep, Amit Kumar Pandey, Swayam Prakash Srivastava, Rahul Srivastava, & Malabika Datta. (2011). Comprehensive miRNome and in silico analyses identify the Wntsignaling pathway to be altered in the diabetic liver. Molecular BioSystems. 7(12). 3234–3244. 36 indexed citations
18.
Verma, Gaurav & Malabika Datta. (2010). IL-1β induces ER stress in a JNK dependent manner that determines cell death in human pancreatic epithelial MIA PaCa-2 cells. APOPTOSIS. 15(7). 864–876. 73 indexed citations
19.
20.
Dey, Debleena, Dipanjan Basu, Malabika Datta, et al.. (2005). Inhibition of Insulin Receptor Gene Expression and Insulin Signaling by Fatty Acid: Interplay of PKC Isoforms Therein. Cellular Physiology and Biochemistry. 16(4-6). 217–228. 47 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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