Debarka Sengupta

3.5k total citations · 1 hit paper
57 papers, 1.6k citations indexed

About

Debarka Sengupta is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Debarka Sengupta has authored 57 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 21 papers in Cancer Research and 9 papers in Artificial Intelligence. Recurrent topics in Debarka Sengupta's work include Single-cell and spatial transcriptomics (21 papers), Gene expression and cancer classification (10 papers) and MicroRNA in disease regulation (9 papers). Debarka Sengupta is often cited by papers focused on Single-cell and spatial transcriptomics (21 papers), Gene expression and cancer classification (10 papers) and MicroRNA in disease regulation (9 papers). Debarka Sengupta collaborates with scholars based in India, Australia and Singapore. Debarka Sengupta's co-authors include Sanghamitra Bandyopadhyay, Angshul Majumdar, Aanchal Mongia, Say Li Kong, Lawrence JK Wee, Yuliana Tan, Elise T. Courtois, Huipeng Li, Wah Siew Tan and Jolene Jie Lin Goh and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Debarka Sengupta

53 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

2017 722 citations Debarka Sengupta, Say Li Kong et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Debarka Sengupta India	18	1.0k	537	433	225	130	57	1.6k
Biao Luo China	17	1.6k 1.6×	389 0.7×	328 0.8×	215 1.0×	90 0.7×	31	2.5k
Venkata Satagopam Luxembourg	19	1.1k 1.0×	158 0.3×	141 0.3×	190 0.8×	64 0.5×	66	1.6k
Frank Bergmann Germany	35	1.8k 1.7×	472 0.9×	1.7k 4.0×	315 1.4×	181 1.4×	98	4.1k
Keyan Salari United States	22	1.3k 1.2×	693 1.3×	599 1.4×	127 0.6×	89 0.7×	52	2.4k
Oren Litvin United States	9	1.9k 1.8×	337 0.6×	314 0.7×	527 2.3×	67 0.5×	12	2.6k
Dongya Jia United States	22	1.6k 1.6×	1.1k 2.1×	1.1k 2.6×	133 0.6×	157 1.2×	50	2.7k
Žiga Avsec Germany	10	2.1k 2.0×	290 0.5×	88 0.2×	138 0.6×	51 0.4×	14	2.8k
Jiarui Ding Canada	19	1.7k 1.6×	730 1.4×	269 0.6×	250 1.1×	37 0.3×	50	2.5k
Michael Khan United Kingdom	24	2.0k 1.9×	493 0.9×	796 1.8×	258 1.1×	99 0.8×	62	3.4k
Stella Pelengaris United Kingdom	20	1.9k 1.8×	412 0.8×	812 1.9×	242 1.1×	53 0.4×	37	2.9k

Countries citing papers authored by Debarka Sengupta

Since Specialization

Citations

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

Fields of papers citing papers by Debarka Sengupta

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debarka Sengupta

This figure shows the co-authorship network connecting the top 25 collaborators of Debarka Sengupta. A scholar is included among the top collaborators of Debarka Sengupta 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 Debarka Sengupta. Debarka Sengupta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Bhattacharya, N, Anja Rockstroh, Cynthia Fourgeux, et al.. (2025). Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra. eLife. 13.

Sengupta, Debarka, et al.. (2025). Biophysical and molecular characterization of HuR inhibitors, CMLD-2 and Dihydrotanshinone I (DHTS), in Triple Negative Breast Cancer (TNBC). International Journal of Biological Macromolecules. 320(Pt 2). 145848–145848. 1 indexed citations

Kumar, Naveen, M. L. Chawla, N Bhattacharya, et al.. (2024). Non-canonical NF-κB signaling limits the tolerogenic β-catenin-Raldh2 axis in gut dendritic cells to exacerbate intestinal pathologies. The EMBO Journal. 43(18). 3895–3915. 5 indexed citations

Sharma, Divya, Neha Jha, Apoorva Gupta, et al.. (2024). Literature mining discerns latent disease–gene relationships. Bioinformatics. 40(4). 2 indexed citations

Bhattacharya, N, Anja Rockstroh, Cynthia Fourgeux, et al.. (2024). Artificial intelligence driven tumor risk stratification from single-cell transcriptomics using phenotype algebra. eLife. 13.

Sengupta, Debarka, et al.. (2023). Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism. Placenta. 140. 109–116. 7 indexed citations

Fantini, Damiano, Muhammad Zahoor, Veronika Reiterer, et al.. (2023). A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression. PLoS Computational Biology. 19(4). e1010995–e1010995. 3 indexed citations

Mishra, Shreya, et al.. (2023). Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis. Genome Research. 33(2). 218–231.

Rockstroh, Anja, Melanie Lehman, Apoorva Gupta, et al.. (2022). Gene expression based inference of cancer drug sensitivity. Nature Communications. 13(1). 5680–5680. 70 indexed citations

10.

Goel, Anurag, N Bhattacharya, Yi Fang Lee, et al.. (2022). Marker-free characterization of full-length transcriptomes of single live circulating tumor cells. Genome Research. 33(1). 80–95. 5 indexed citations

11.

Singh, Vijay Pal, et al.. (2022). Artificial intelligence uncovers carcinogenic human metabolites. Nature Chemical Biology. 18(11). 1204–1213. 12 indexed citations

12.

Sengupta, Debarka, et al.. (2021). Enhash: A Fast Streaming Algorithm For Concept Drift Detection. 59–64. 1 indexed citations

13.

Biswas, Aditya, Chad Sanada, Ujjwal Maulik, et al.. (2021). Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-seq data. Genome Research. 31(4). 689–697. 10 indexed citations

14.

Samydurai, Sudhagar, Say Li Kong, Zhengwei Wu, et al.. (2020). UniPath: a uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles. Nucleic Acids Research. 49(3). e13–e13. 12 indexed citations

15.

Iyer, Arvind, Shreya Sharma, Kishore Hari, et al.. (2020). Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. Journal of Clinical Medicine. 9(4). 1206–1206. 28 indexed citations

16.

Thakral, Deepshi, Himanshu Pant, Pramod Kumar Verma, et al.. (2020). Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC. BMC Genomics. 21(1). 744–744. 15 indexed citations

17.

Mongia, Aanchal, Debarka Sengupta, & Angshul Majumdar. (2019). McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data. Frontiers in Genetics. 10. 9–9. 55 indexed citations

18.

Sengupta, Debarka & Sanghamitra Bandyopadhyay. (2013). Topological patterns in microRNA–gene regulatory network: studies in colorectal and breast cancer. Molecular BioSystems. 9(6). 1360–1371. 25 indexed citations

19.

Bandyopadhyay, Sanghamitra, Debarka Sengupta, & Ujjwal Maulik. (2013). GRF: A Greedy Rank Fusion Algorithm for Combining MicroRNA Target Orderings. Molecular Informatics. 32(8). 685–691. 1 indexed citations

20.

Sengupta, Debarka & Sanghamitra Bandyopadhyay. (2011). Participation of microRNAs in human interactome: extraction of microRNA–microRNA regulations. Molecular BioSystems. 7(6). 1966–1973. 18 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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