Sumanta Ray

1.7k total citations · 1 hit paper
49 papers, 990 citations indexed

About

Sumanta Ray is a scholar working on Molecular Biology, Computational Theory and Mathematics and Virology. According to data from OpenAlex, Sumanta Ray has authored 49 papers receiving a total of 990 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 6 papers in Virology. Recurrent topics in Sumanta Ray's work include Bioinformatics and Genomic Networks (30 papers), Gene expression and cancer classification (13 papers) and Machine Learning in Bioinformatics (12 papers). Sumanta Ray is often cited by papers focused on Bioinformatics and Genomic Networks (30 papers), Gene expression and cancer classification (13 papers) and Machine Learning in Bioinformatics (12 papers). Sumanta Ray collaborates with scholars based in India, United States and United Kingdom. Sumanta Ray's co-authors include Sutapa Bose, Vivek Rai, Swapan K. Ghosh, Sujoy Pal, Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Ujjwal Maulik, A F Muller, Michael P. Delaney and C. Samanta and has published in prestigious journals such as PLoS ONE, Scientific Reports and Frontiers in Immunology.

In The Last Decade

Sumanta Ray

46 papers receiving 959 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.

A Broader View: Microbial Enzymes and Their Relevance in Industries, Medicine, and Beyond

2013 397 citations Sumanta Ray et al. profile →

Peers

Sumanta Ray

MB

POLLU

BIOTE

BE

BIOMA

Kaveh Kavousi Iran

Cristina Fernández Spain

Zheng Zhong China

C. D. de Gooijer Netherlands

Marco P. C. Marques Portugal

Robert D. Tanner United States

A. Wahl Netherlands

Donghyuk Kim South Korea

Ezequiel Franco‐Lara Germany

Kaveh Kavousi Iran

Sumanta Ray

879 ×1.7

MB

68 ×0.4

POLLU

332 ×2.0

BIOTE

333 ×2.6

BE

66 ×0.5

BIOMA

Citations per year, relative to Sumanta Ray Sumanta Ray (= 1×) peers Kaveh Kavousi

Countries citing papers authored by Sumanta Ray

Since Specialization

Citations

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

Fields of papers citing papers by Sumanta Ray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sumanta Ray

This figure shows the co-authorship network connecting the top 25 collaborators of Sumanta Ray. A scholar is included among the top collaborators of Sumanta Ray 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 Sumanta Ray. Sumanta Ray 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

1.

Wigerblad, Gustaf, Sumanta Ray, Thomas Finnie, et al.. (2025). A mathematical framework for human neutrophil state transitions inferred from single-cell RNA sequence data. Frontiers in Immunology. 16. 1654015–1654015.

2.

Ray, Sumanta, et al.. (2022). A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data. PLoS Computational Biology. 18(3). e1009600–e1009600. 6 indexed citations

3.

Ray, Sumanta, et al.. (2022). A Regularized Multi-Task Learning Approach for Cell Type Detection in Single-Cell RNA Sequencing Data. Frontiers in Genetics. 13. 788832–788832. 4 indexed citations

4.

Ghosh, Abhik, et al.. (2021). sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data. Briefings in Bioinformatics. 23(2). 12 indexed citations

5.

Ray, Sumanta, et al.. (2021). Pan-cancer classification by regularized multi-task learning. Scientific Reports. 11(1). 24252–24252. 8 indexed citations

6.

Ray, Sumanta, et al.. (2021). Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model. Scientific Reports. 11(1). 7853–7853. 12 indexed citations

7.

Ray, Sumanta, et al.. (2021). RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data. PLoS Computational Biology. 17(10). e1009464–e1009464. 10 indexed citations

8.

Ray, Sumanta, et al.. (2021). Identification of key immune regulatory genes in HIV-1 progression. Gene. 792. 145735–145735. 3 indexed citations

9.

Ray, Sumanta, et al.. (2021). DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation. PLoS ONE. 16(2). e0246920–e0246920. 8 indexed citations

10.

Pyne, Saumyadipta, et al.. (2020). Transition from Social Vulnerability to Resiliency vis-a-vis COVID-19. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 18(1). 197–208. 1 indexed citations

11.

Ray, Sumanta, et al.. (2020). CODC: a Copula-based model to identify differential coexpression. npj Systems Biology and Applications. 6(1). 20–20. 10 indexed citations

12.

Maulik, Ujjwal, et al.. (2017). Identifying protein complexes in PPI network using non-cooperative sequential game. Scientific Reports. 7(1). 8410–8410. 8 indexed citations

13.

Ray, Sumanta & Ujjwal Maulik. (2017). Identifying differentially coexpressed module during HIV disease progression: A multiobjective approach. Scientific Reports. 7(1). 86–86. 12 indexed citations

14.

Ray, Sumanta, et al.. (2017). A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer’s disease progression. BMC Bioinformatics. 18(1). 579–579. 14 indexed citations

15.

Ray, Sumanta, et al.. (2017). Preservation affinity in consensus modules among stages of HIV-1 progression. BMC Bioinformatics. 18(1). 181–181. 9 indexed citations

16.

Ray, Sumanta & Sanghamitra Bandyopadhyay. (2016). A NMF based approach for integrating multiple data sources to predict HIV-1–human PPIs. BMC Bioinformatics. 17(1). 121–121. 16 indexed citations

17.

Bandyopadhyay, Sanghamitra, Sumanta Ray, Anirban Mukhopadhyay, & Ujjwal Maulik. (2015). A multiobjective approach for identifying protein complexes and studying their association in multiple disorders. Algorithms for Molecular Biology. 10(1). 24–24. 18 indexed citations

18.

Mukhopadhyay, Anirban, Sumanta Ray, & Ujjwal Maulik. (2014). Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach. BMC Bioinformatics. 15(1). 26–26. 34 indexed citations

19.

Ray, Sumanta, Sanghamitra Bandyopadhyay, Anirban Mukhopadhyay, & Ujjwal Maulik. (2013). Incorporating fuzzy semantic similarity measure in detecting human protein complexes in PPI network: A multiobjective approach. 1–8. 5 indexed citations

20.

Mukhopadhyay, Anirban, et al.. (2012). Detecting protein complexes in a PPI network: a gene ontology based multi-objective evolutionary approach. Molecular BioSystems. 8(11). 3036–3048. 37 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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