Sushmita Paul

615 total citations
27 papers, 357 citations indexed

About

Sushmita Paul is a scholar working on Molecular Biology, Cancer Research and Pharmacology. According to data from OpenAlex, Sushmita Paul has authored 27 papers receiving a total of 357 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 10 papers in Cancer Research and 3 papers in Pharmacology. Recurrent topics in Sushmita Paul's work include MicroRNA in disease regulation (10 papers), Cancer-related molecular mechanisms research (9 papers) and Gene expression and cancer classification (9 papers). Sushmita Paul is often cited by papers focused on MicroRNA in disease regulation (10 papers), Cancer-related molecular mechanisms research (9 papers) and Gene expression and cancer classification (9 papers). Sushmita Paul collaborates with scholars based in India, Germany and Poland. Sushmita Paul's co-authors include Pradipta Maji, Madhumita Madhumita, Julio Vera, Stephen Reid, Tobias Bäuerle, Sophia Sonnewald, Regine Schneider‐Stock, Nicole Hannemann, Georg Schett and Arndt Hartmann and has published in prestigious journals such as The Journal of Immunology, Scientific Reports and BMC Bioinformatics.

In The Last Decade

Sushmita Paul

23 papers receiving 351 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sushmita Paul India 11 240 95 68 42 40 27 357
Philip Stegmaier Germany 13 304 1.3× 70 0.7× 77 1.1× 53 1.3× 34 0.8× 21 498
Binsheng Gong United States 11 483 2.0× 240 2.5× 41 0.6× 41 1.0× 58 1.4× 38 631
Chatchawit Aporntewan Thailand 11 241 1.0× 75 0.8× 36 0.5× 59 1.4× 43 1.1× 36 459
Tiziana Sanavia Italy 16 463 1.9× 84 0.9× 39 0.6× 48 1.1× 34 0.8× 40 699
Paul Fogel France 11 199 0.8× 64 0.7× 61 0.9× 44 1.0× 10 0.3× 27 446
Jonathan Wagg Switzerland 8 480 2.0× 44 0.5× 46 0.7× 82 2.0× 65 1.6× 20 645
Tomoyuki Uchida Japan 9 116 0.5× 30 0.3× 53 0.8× 40 1.0× 20 0.5× 79 351
Yubo Fan United States 6 206 0.9× 57 0.6× 24 0.4× 35 0.8× 29 0.7× 9 309
Krzysztof Simek Poland 9 208 0.9× 80 0.8× 38 0.6× 67 1.6× 21 0.5× 15 387

Countries citing papers authored by Sushmita Paul

Since Specialization
Citations

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

Fields of papers citing papers by Sushmita Paul

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sushmita Paul

This figure shows the co-authorship network connecting the top 25 collaborators of Sushmita Paul. A scholar is included among the top collaborators of Sushmita Paul 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 Sushmita Paul. Sushmita Paul 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.
2.
Paul, Sushmita, et al.. (2023). Exploration of Mangroves Associated Microbes for Bioactive Metabolites. International Journal of Plant and Environment. 9(3). 202–209.
3.
Madhumita, Madhumita, et al.. (2022). Recursive integration of synergised graph representations of multi-omics data for cancer subtypes identification. Scientific Reports. 12(1). 15629–15629.
4.
Madhumita, Madhumita & Sushmita Paul. (2022). Capturing the latent space of an Autoencoder for multi-omics integration and cancer subtyping. Computers in Biology and Medicine. 148. 105832–105832. 20 indexed citations
5.
Madhumita, Madhumita & Sushmita Paul. (2022). A review on methods for predicting miRNA–mRNA regulatory modules. Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics. 19(3). 11 indexed citations
6.
Madhumita, Madhumita, et al.. (2021). CorGO: An Integrated Method for Clustering Functionally Similar Genes. Interdisciplinary Sciences Computational Life Sciences. 13(4). 624–637. 1 indexed citations
7.
Paul, Sushmita, Markus Eckstein, Julienne K. Muenzner, et al.. (2020). EMT transcription factor ZEB1 alters the epigenetic landscape of colorectal cancer cells. Cell Death and Disease. 11(2). 147–147. 62 indexed citations
8.
Paul, Sushmita, et al.. (2019). Genome-wide analysis of multi-view data of miRNA-seq to identify miRNA biomarkers for stomach cancer. Journal of Biomedical Informatics. 97. 103254–103254. 6 indexed citations
9.
Sharma, Nidhi, et al.. (2019). Differential Expression Profile of NLRs and AIM2 in Glioma and Implications for NLRP12 in Glioblastoma. Scientific Reports. 9(1). 8480–8480. 34 indexed citations
10.
Paul, Sushmita. (2019). Integration of miRNA and mRNA Expression Data for Understanding Etiology of Gynecologic Cancers. Methods in molecular biology. 1912. 323–338. 7 indexed citations
11.
Paul, Sushmita, et al.. (2017). Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering. Scientific Reports. 7(1). 42809–42809. 10 indexed citations
12.
Paul, Sushmita, et al.. (2017). An Integrated Approach for Identification of Functionally Similar MicroRNAs in Colorectal Cancer. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 16(1). 183–192. 7 indexed citations
13.
Nikolov, Svetoslav, Xin Lai, Martin Eberhardt, et al.. (2016). Model-based genotype-phenotype mapping used to investigate gene signatures of immune sensitivity and resistance in melanoma micrometastasis. Scientific Reports. 6(1). 24967–24967. 16 indexed citations
14.
Maji, Pradipta, et al.. (2016). RelSim: An integrated method to identify disease genes using gene expression profiles and PPIN based similarity measure. Information Sciences. 384. 110–125. 24 indexed citations
15.
Paul, Sushmita & Julio Vera. (2015). Rough hypercuboid based supervised clustering of miRNAs. Molecular BioSystems. 11(7). 2068–2081. 4 indexed citations
16.
Paul, Sushmita & Pradipta Maji. (2015). Gene expression and protein–protein interaction data for identification of colon cancer related genes using f-information measures. Natural Computing. 15(3). 449–463. 7 indexed citations
17.
Paul, Sushmita & Pradipta Maji. (2014). City block distance and rough-fuzzy clustering for identification of co-expressed microRNAs. Molecular BioSystems. 10(6). 1509–1523. 16 indexed citations
18.
Paul, Sushmita & Pradipta Maji. (2013). μHEM for identification of differentially expressed miRNAs using hypercuboid equivalence partition matrix. BMC Bioinformatics. 14(1). 266–266. 8 indexed citations
19.
Paul, Sushmita & Pradipta Maji. (2012). Gene ontology based quantitative index to select functionally diverse genes. International Journal of Machine Learning and Cybernetics. 5(2). 245–262. 5 indexed citations
20.
Maji, Pradipta & Sushmita Paul. (2011). Microarray Time-Series Data Clustering Using Rough-Fuzzy C-Means Algorithm. 269–272. 13 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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