Tapas Bhadra

518 citations
22 papers · 351 · h-index 10

Impact in

Papers in

Tapas Bhadra

20 papers receiving 346 citations

Peers

Tapas Bhadra
Comparison fields: 5 of 73
  • Artificial Intelligence 140
  • Computer Vision and Pattern Recognition 81
  • Cancer Research 36
  • Molecular Biology 140
  • Computational Theory and Mathematics 33
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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 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201349
2 201448
3 201739
4 202130
5 202028
6 201725
7 202224
8 202223
9 201220
10 201220
11 20188
12 20137
13
Variable Weighted Maximal Relevance Minimal Redundancy Criterion for Feature Selection Using Normalized Mutual Information.
20157
14 20236
15 20215
16 20225
17 20233
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 Information Systems, having authored 22 papers that have together received 351 indexed citations. Recurring topics across this work include Gene expression and cancer classification (11 papers), Bioinformatics and Genomic Networks (9 papers), Face and Expression Recognition (6 papers), Machine Learning in Bioinformatics (5 papers), Single-cell and spatial transcriptomics (3 papers), Metaheuristic Optimization Algorithms Research (3 papers), MicroRNA in disease regulation (3 papers) and Data Mining Algorithms and Applications (2 papers). The work is most often cited by research in Artificial Intelligence (140 citations), Computer Vision and Pattern Recognition (81 citations), Cancer Research (36 citations), Molecular Biology (140 citations) and Computational Theory and Mathematics (33 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, Thomas Lengauer, Malay Bhattacharyya, Lars Feuerbach, Aimin Li and Pawan Kumar Singh. Their work appears in journals such as IEEE Transactions on NanoBioscience, Expert Systems with Applications, IEEE Transactions on Systems Man and Cybernetics Systems, IEEE Transactions on Emerging Topics in Computational Intelligence and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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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