Prosenjit Paul

1.8k total citations · 1 hit paper
23 papers, 1.2k citations indexed

About

Prosenjit Paul is a scholar working on Molecular Biology, Cancer Research and Organic Chemistry. According to data from OpenAlex, Prosenjit Paul has authored 23 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 4 papers in Cancer Research and 2 papers in Organic Chemistry. Recurrent topics in Prosenjit Paul's work include RNA and protein synthesis mechanisms (11 papers), Genomics and Phylogenetic Studies (10 papers) and RNA modifications and cancer (8 papers). Prosenjit Paul is often cited by papers focused on RNA and protein synthesis mechanisms (11 papers), Genomics and Phylogenetic Studies (10 papers) and RNA modifications and cancer (8 papers). Prosenjit Paul collaborates with scholars based in India, Bangladesh and United States. Prosenjit Paul's co-authors include Supriyo Chakraborty, Binata Halder, Arif Uddin, Supriyo Chakraborty, Arup Kumar Malakar, Debasree Sarkar, Md. Emdad Hossain, Md. Mahmudul Islam, Md. Abdur Rouf and Md. Khairul Alam and has published in prestigious journals such as SHILAP Revista de lepidopterología, Gene and Journal of Cellular Physiology.

In The Last Decade

Prosenjit Paul

21 papers receiving 1.1k citations

Hit Papers

A review on coronary artery disease, its risk factors, an... 2019 2026 2021 2023 2019 200 400 600
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.

  1. A review on coronary artery disease, its risk factors, and therapeutics
    2019 681 citations Binata Halder, Prosenjit Paul et al. Journal of Cellular Physiology profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Prosenjit Paul India 10 501 315 281 171 111 23 1.2k
Peiying Zhang China 19 520 1.0× 229 0.7× 274 1.0× 175 1.0× 64 0.6× 42 1.3k
Masataka Nishiga Japan 14 418 0.8× 171 0.5× 481 1.7× 204 1.2× 68 0.6× 27 1.6k
Hong Ma China 17 489 1.0× 190 0.6× 291 1.0× 234 1.4× 84 0.8× 76 1.2k
Mingming Yang China 17 438 0.9× 180 0.6× 120 0.4× 137 0.8× 61 0.5× 80 966
Young Joo Kim South Korea 21 598 1.2× 164 0.5× 183 0.7× 90 0.5× 93 0.8× 82 1.3k
Bing Han China 21 411 0.8× 153 0.5× 312 1.1× 435 2.5× 67 0.6× 104 1.4k
Jing Yan China 19 604 1.2× 142 0.5× 157 0.6× 154 0.9× 120 1.1× 89 1.5k
Franciszek Burdan Poland 22 352 0.7× 173 0.5× 143 0.5× 259 1.5× 53 0.5× 146 1.6k

Countries citing papers authored by Prosenjit Paul

Since Specialization
Citations

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

Fields of papers citing papers by Prosenjit Paul

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prosenjit Paul

This figure shows the co-authorship network connecting the top 25 collaborators of Prosenjit Paul. A scholar is included among the top collaborators of Prosenjit Paul 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 Prosenjit Paul. Prosenjit Paul 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
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Paul, Prosenjit, et al.. (2024). Design, synthesis, characterization, antimicrobial activity, cytotoxicity, molecular docking, and in-silico ADMET analysis of the novel cefuroxime derivatives. SHILAP Revista de lepidopterología. 10. 100129–100129. 8 indexed citations
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Halder, Binata, et al.. (2019). Genetic evolution and codon usage analysis of NKX-2.5 gene governing heart development in some mammals. Genomics. 112(2). 1319–1329. 10 indexed citations
6.
Paul, Prosenjit, Arup Kumar Malakar, & Supriyo Chakraborty. (2019). The significance of gene mutations across eight major cancer types. Mutation Research/Reviews in Mutation Research. 781. 88–99. 22 indexed citations
7.
Paul, Prosenjit, et al.. (2019). Computational identification of conserved microRNAs and functional annotation of their target genes in Citrus limon. South African Journal of Botany. 130. 109–116. 6 indexed citations
8.
Paul, Prosenjit, et al.. (2017). Interplay between miRNAs and human diseases. Journal of Cellular Physiology. 233(3). 2007–2018. 332 indexed citations
9.
Paul, Prosenjit, Arup Kumar Malakar, & Supriyo Chakraborty. (2017). Codon usage and amino acid usage influence genes expression level. Genetica. 146(1). 53–63. 13 indexed citations
10.
Chakraborty, Supriyo, et al.. (2017). Codon usage and expression level of human mitochondrial 13 protein coding genes across six continents. Mitochondrion. 42. 64–76. 11 indexed citations
11.
Paul, Prosenjit, Arup Kumar Malakar, & Supriyo Chakraborty. (2017). Compositional bias coupled with selection and mutation pressure drives codon usage in Brassica campestris genes. Food Science and Biotechnology. 27(3). 725–733. 7 indexed citations
12.
Paul, Prosenjit, et al.. (2016). Recombination hotspots: Models and tools for detection. DNA repair. 40. 47–56. 15 indexed citations
13.
Malakar, Arup Kumar, Binata Halder, Prosenjit Paul, & Supriyo Chakraborty. (2016). Cytochrome P450 genes in coronary artery diseases: Codon usage analysis reveals genomic GC adaptation. Gene. 590(1). 35–43. 12 indexed citations
14.
Paul, Prosenjit, et al.. (2016). Codon usage bias analysis for the coding sequences of Camellia sinensis and Brassica campestris. AFRICAN JOURNAL OF BIOTECHNOLOGY. 15(8). 236–251. 2 indexed citations
15.
Malakar, Arup Kumar, et al.. (2015). Allele frequency for Cystic fibrosis in Indians vis-a/-vis global populations. Bioinformation. 11(7). 348–352. 5 indexed citations
16.
Chakraborty, Supriyo & Prosenjit Paul. (2015). Guanine and Cytosine at the Second Codon Position Influence Gene Expression in Cereals. Proceedings of the National Academy of Sciences India Section B Biological Sciences. 85(4). 1105–1115. 2 indexed citations
17.
Chakraborty, Supriyo, et al.. (2014). A cross talk between codon usage bias in human oncogenes. Bioinformation. 10(5). 256–262. 10 indexed citations
18.
Chakraborty, Supriyo & Prosenjit Paul. (2014). GC2 Biology Dictates Gene Expressivity in <i>Camellia sinensis</i>. 1 indexed citations
19.
Chakraborty, Supriyo, et al.. (2014). Codon Usage Bias Prefers AT Bases in Coding Sequences Among the Essential Genes of <i>Haemophilus influenzae</i>. Notulae Scientia Biologicae. 6(4). 417–421. 5 indexed citations
20.
Paul, Prosenjit & Supriyo Chakraborty. (2013). Computational prediction of submergence responsive microRNA and their binding position within the genome of Oryza sativa. Bioinformation. 9(17). 858–863. 7 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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