Tapas Bhadra

498 citations
21 papers · 339 · h-index 10

Impact in

Papers in

Tapas Bhadra

20 papers receiving 331 citations

Peers

Tapas Bhadra
Comparison fields: 5 of 78
  • Artificial Intelligence 139
  • Computer Vision and Pattern Recognition 80
  • Cancer Research 39
  • Computational Theory and Mathematics 36
  • Molecular Biology 150
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Guimin Qin China
Daniel Le United States
Elham Pashaei Türkiye
Daqing Yang China
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Erdal Taşçı Türkiye
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Shemim Begum India
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Citations per field
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Citations per year

Countries citing papers authored by Tapas Bhadra

Since Specialization
Citations

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

Fields of papers citing papers by Tapas Bhadra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Tapas Bhadra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tapas Bhadra Line = papers co-authored together Tapas Bhadra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201448
2 201348
3 201739
4 202130
5 202027
6 201724
7 202222
8 201220
9 202220
10 201220
11 20137
12
Variable Weighted Maximal Relevance Minimal Redundancy Criterion for Feature Selection Using Normalized Mutual Information.
20157
13 20187
14 20236
15 20215
16 20223
17 20232
18 20192
19 20241
20 20221

About Tapas Bhadra

Tapas Bhadra is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition, Artificial Intelligence, Cancer Research and Computational Theory and Mathematics, having authored 21 papers that have together received 339 indexed citations. Recurring topics across this work include Gene expression and cancer classification (11 papers), Bioinformatics and Genomic Networks (10 papers), Machine Learning in Bioinformatics (6 papers), Face and Expression Recognition (6 papers), Single-cell and spatial transcriptomics (3 papers), MicroRNA in disease regulation (3 papers), Metaheuristic Optimization Algorithms Research (3 papers) and Evolutionary Algorithms and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (139 citations), Computer Vision and Pattern Recognition (80 citations), Cancer Research (39 citations), Computational Theory and Mathematics (36 citations) and Molecular Biology (150 citations). Tapas Bhadra has collaborated with scholars based in India, United States and China. Frequent co-authors include Sanghamitra Bandyopadhyay, Saurav Mallik, Ujjwal Maulik, Zhongming Zhao, Pabitra Mitra, Malay Bhattacharyya, Thomas Lengauer, Lars Feuerbach, Pawan Kumar Singh and Arup Roy. Their work appears in journals such as IEEE Transactions on NanoBioscience, Information Sciences, BMC Bioinformatics, Expert Systems with Applications and IEEE Transactions on Systems Man and Cybernetics Systems.

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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