Tapas Bhadra

498 total citations
21 papers, 339 citations indexed

About

Tapas Bhadra is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Tapas Bhadra has authored 21 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 7 papers in Computer Vision and Pattern Recognition and 6 papers in Artificial Intelligence. Recurrent topics in Tapas Bhadra's work include Gene expression and cancer classification (11 papers), Bioinformatics and Genomic Networks (10 papers) and Machine Learning in Bioinformatics (6 papers). Tapas Bhadra is often cited by papers focused on Gene expression and cancer classification (11 papers), Bioinformatics and Genomic Networks (10 papers) and Machine Learning in Bioinformatics (6 papers). Tapas Bhadra collaborates with scholars based in India, United States and China. Tapas Bhadra's co-authors include Sanghamitra Bandyopadhyay, Saurav Mallik, Ujjwal Maulik, Zhongming Zhao, Pabitra Mitra, Malay Bhattacharyya, Lars Feuerbach, Thomas Lengauer, Aimin Li and Pawan Kumar Singh and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and BMC Bioinformatics.

In The Last Decade

Tapas Bhadra

20 papers receiving 331 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tapas Bhadra India 10 150 139 80 39 36 21 339
Di He China 11 147 1.0× 265 1.9× 137 1.7× 14 0.4× 50 1.4× 26 522
Erdal Taşçı Türkiye 11 72 0.5× 197 1.4× 85 1.1× 29 0.7× 29 0.8× 39 532
Elham Pashaei Türkiye 14 243 1.6× 241 1.7× 76 0.9× 82 2.1× 43 1.2× 27 603
Yisong Wang China 12 95 0.6× 171 1.2× 49 0.6× 14 0.4× 20 0.6× 57 428
Mingzhu Lu China 11 137 0.9× 140 1.0× 173 2.2× 70 1.8× 13 0.4× 29 483
Marco Frasca Italy 11 186 1.2× 102 0.7× 42 0.5× 22 0.6× 55 1.5× 31 369
Shubhra Sankar Ray India 12 202 1.3× 184 1.3× 42 0.5× 34 0.9× 106 2.9× 43 451
Guimin Qin China 12 197 1.3× 107 0.8× 34 0.4× 59 1.5× 36 1.0× 33 430

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-authorship network of co-authors of Tapas Bhadra

This figure shows the co-authorship network connecting the top 25 collaborators of Tapas Bhadra. A scholar is included among the top collaborators of Tapas Bhadra 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 Tapas Bhadra. Tapas Bhadra 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
1.
2.
Ghosh, Soumadip, et al.. (2024). DeMoS: dense module based gene signature detection through quasi-clique: an application to cervical cancer prognosis. Network Modeling Analysis in Health Informatics and Bioinformatics. 13(1). 1 indexed citations
3.
4.
Mallik, Saurav, Tapas Bhadra, Arup Roy, et al.. (2023). Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering. Mathematics. 11(20). 4315–4315. 6 indexed citations
5.
6.
Bhadra, Tapas, et al.. (2022). Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer. BMC Bioinformatics. 23(S3). 153–153. 20 indexed citations
7.
8.
Bhadra, Tapas & Ujjwal Maulik. (2022). Unsupervised Feature Selection Using Iterative Shrinking and Expansion Algorithm. IEEE Transactions on Emerging Topics in Computational Intelligence. 6(6). 1453–1462. 3 indexed citations
9.
Bhadra, Tapas, et al.. (2021). Unsupervised Feature Selection Using an Integrated Strategy of Hierarchical Clustering With Singular Value Decomposition: An Integrative Biomarker Discovery Method With Application to Acute Myeloid Leukemia. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19(3). 1354–1364. 5 indexed citations
10.
Bhadra, Tapas & Sanghamitra Bandyopadhyay. (2021). Supervised feature selection using integration of densest subgraph finding with floating forward–backward search. Information Sciences. 566. 1–18. 30 indexed citations
11.
12.
Mallik, Saurav, et al.. (2019). A Multi-classifier Model to Identify Mitochondrial Respiratory Gene Signatures in Human Cancer. 117. 1928–1935. 2 indexed citations
13.
Mallik, Saurav, et al.. (2018). DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein–Protein Interaction Profiles. IEEE Transactions on NanoBioscience. 17(2). 117–125. 7 indexed citations
14.
Mallik, Saurav, Tapas Bhadra, & Ujjwal Maulik. (2017). Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data. IEEE Transactions on NanoBioscience. 16(1). 3–10. 39 indexed citations
15.
Bandyopadhyay, Sanghamitra, Tapas Bhadra, & Ujjwal Maulik. (2015). Variable Weighted Maximal Relevance Minimal Redundancy Criterion for Feature Selection Using Normalized Mutual Information.. 25. 189–213. 7 indexed citations
16.
Bhadra, Tapas & Sanghamitra Bandyopadhyay. (2014). Unsupervised feature selection using an improved version of Differential Evolution. Expert Systems with Applications. 42(8). 4042–4053. 48 indexed citations
17.
Bhadra, Tapas, Malay Bhattacharyya, Lars Feuerbach, Thomas Lengauer, & Sanghamitra Bandyopadhyay. (2013). DNA Methylation Patterns Facilitate the Identification of MicroRNA Transcription Start Sites: A Brain-Specific Study. PLoS ONE. 8(6). e66722–e66722. 7 indexed citations
18.
Bandyopadhyay, Sanghamitra, Tapas Bhadra, Pabitra Mitra, & Ujjwal Maulik. (2013). Integration of dense subgraph finding with feature clustering for unsupervised feature selection. Pattern Recognition Letters. 40. 104–112. 48 indexed citations
19.
Bhattacharyya, Malay, Lars Feuerbach, Tapas Bhadra, Thomas Lengauer, & Sanghamitra Bandyopadhyay. (2012). MicroRNA Transcription Start Site Prediction with Multi-objective Feature Selection. Statistical Applications in Genetics and Molecular Biology. 11(1). Article 6–Article 6. 20 indexed citations
20.
Bhadra, Tapas, Sanghamitra Bandyopadhyay, & Ujjwal Maulik. (2012). Differential Evolution Based Optimization of SVM Parameters for Meta Classifier Design. Procedia Technology. 4. 50–57. 20 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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