Debashis Ghosh

2.0k citations
47 papers · 1.4k indexed · h-index 17
Topics
Gene expression and cancer classification (21 papers)Statistical Methods in Clinical Trials (8 papers)Molecular Biology Techniques and Applications (8 papers)

In The Last Decade

Debashis Ghosh

45 papers receiving 1.4k citations

Peers

Debashis Ghosh
Comparison fields: 5 of 129
  • Molecular Biology 825
  • Genetics 339
  • Immunology 156
  • Cancer Research 122
  • Spectroscopy 111
Replace Hae‐Won Uh with:
Hae‐Won Uh Netherlands
Anat Reiner‐Benaim Israel
Alan R. Dabney United States
Peter Groenen Switzerland
Grzegorz A. Rempała United States
Claudia Rangel‐Escareño Mexico
Chad Ray United States
Abhay Jere India
Joseph P. Menetski United States
David Engler United States
Debashis Ghosh relative to Hae‐Won Uh Netherlands Hae‐Won Uh's profile →
Citations per field
00.5×1.5×1.9×
Hae‐Won Uh · 1×
Citations per year

Countries citing papers authored by Debashis Ghosh

Since Specialization
Citations

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

Fields of papers citing papers by Debashis Ghosh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debashis Ghosh

This figure shows the co-authorship network connecting the top 25 collaborators of Debashis Ghosh. A scholar is included among the top collaborators of Debashis Ghosh 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 Debashis Ghosh. Debashis Ghosh 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
#WorkIndexed citations
1 21
2 2
3 3
4 376
5
Detecting outlier genes from high-dimensional data: a fuzzy approach
2
6 12
7 51
8 140
9 32
10 5
11 2
12 35
13 60
14 2
15 25
16 4
17 121
18 27
19 4
20 6

About Debashis Ghosh

Debashis Ghosh is a scholar working on Statistics and Probability, Genetics and Molecular Biology, having authored 47 papers that have together received 1.4k indexed citations. Recurring topics across this work include Gene expression and cancer classification (21 papers), Statistical Methods in Clinical Trials (8 papers) and Molecular Biology Techniques and Applications (8 papers). The work is most often cited by research in Genetics (339 citations), Molecular Biology (825 citations) and Statistics and Probability (95 citations). Debashis Ghosh has collaborated with scholars based in United States, Puerto Rico and Germany. Frequent co-authors include Eleanor Feingold, George C. Tseng, Hyungwon Choi, Alexey I. Nesvizhskii, Xihong Lin, Dawei Liu, Laila Poisson, Arul M. Chinnaiyan, Zhaohui Qin and Ronglai Shen. Their work appears in journals such as Nucleic Acids Research, Bioinformatics and NeuroImage.

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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