Arif Uddin

2.6k total citations · 1 hit paper
68 papers, 1.7k citations indexed

About

Arif Uddin is a scholar working on Molecular Biology, Immunology and Plant Science. According to data from OpenAlex, Arif Uddin has authored 68 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Molecular Biology, 7 papers in Immunology and 6 papers in Plant Science. Recurrent topics in Arif Uddin's work include RNA and protein synthesis mechanisms (56 papers), Genomics and Phylogenetic Studies (47 papers) and RNA modifications and cancer (28 papers). Arif Uddin is often cited by papers focused on RNA and protein synthesis mechanisms (56 papers), Genomics and Phylogenetic Studies (47 papers) and RNA modifications and cancer (28 papers). Arif Uddin collaborates with scholars based in India, United States and South Korea. Arif Uddin's co-authors include Supriyo Chakraborty, Supriyo Chakraborty, Prosenjit Paul, Binata Halder, Bornali Deb, Ramin Abdoli, Yashmin Choudhury, Yoon Shin Cho, Nojood Altwaijry and Muhammad Nadeem Asghar and has published in prestigious journals such as Annals of the New York Academy of Sciences, Annals of the Rheumatic Diseases and Gene.

In The Last Decade

Arif Uddin

66 papers receiving 1.7k 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 Arif Uddin 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
Arif Uddin India 20 1.0k 300 204 160 158 68 1.7k
Ching‐Jin Chang Taiwan 26 1.3k 1.2× 146 0.5× 229 1.1× 304 1.9× 136 0.9× 58 2.3k
Hans Christian Beck Denmark 25 1.1k 1.1× 104 0.3× 213 1.0× 178 1.1× 155 1.0× 115 1.9k
Yuen Tan China 18 436 0.4× 231 0.8× 128 0.6× 124 0.8× 102 0.6× 41 1.4k
Carlos Pastor‐Vargas Spain 26 407 0.4× 165 0.6× 116 0.6× 209 1.3× 325 2.1× 130 2.0k
Jiahui Yu China 19 595 0.6× 206 0.7× 83 0.4× 149 0.9× 122 0.8× 59 1.3k
Xuemei Xu China 24 1.1k 1.0× 103 0.3× 134 0.7× 383 2.4× 109 0.7× 107 2.0k
Shan Lü China 21 974 0.9× 106 0.4× 226 1.1× 341 2.1× 133 0.8× 87 1.9k
Yanrong Wang China 18 401 0.4× 88 0.3× 177 0.9× 100 0.6× 98 0.6× 66 1.3k

Countries citing papers authored by Arif Uddin

Since Specialization
Citations

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

Fields of papers citing papers by Arif Uddin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arif Uddin

This figure shows the co-authorship network connecting the top 25 collaborators of Arif Uddin. A scholar is included among the top collaborators of Arif Uddin 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 Arif Uddin. Arif Uddin 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.
Uddin, Arif, et al.. (2024). Understanding the nucleotide composition and patterns of codon usage in the expression of human oral cancer genes. Mutation research. Fundamental and molecular mechanisms of mutagenesis. 829. 111880–111880.
2.
Yao, Qingping, et al.. (2024). OP0262 BIOINFORMATICS-DRIVEN ANALYSIS OF GERMLINE MUTATIONS AND THEIR IMPACT IN ADULTS WITH AUTOINFLAMMATORY DISEASES. Annals of the Rheumatic Diseases. 83. 64–64. 1 indexed citations
3.
Abdoli, Ramin, et al.. (2022). Gaining insights into the compositional constraints and molecular phylogeny of five silkworms mitochondrial genome. International Journal of Biological Macromolecules. 206. 543–552. 10 indexed citations
4.
Deb, Bornali, Arif Uddin, & Supriyo Chakraborty. (2021). Analysis of codon usage of Horseshoe Bat Hepatitis B virus and its host. Virology. 561. 69–79. 9 indexed citations
5.
Deb, Bornali, Arif Uddin, & Supriyo Chakraborty. (2021). Composition, codon usage pattern, protein properties, and influencing factors in the genomes of members of the family Anelloviridae. Archives of Virology. 166(2). 461–474. 28 indexed citations
6.
Uddin, Arif, et al.. (2020). Codon usage pattern and evolutionary forces of mitochondrial ND genes among orders of class Amphibia. Journal of Cellular Physiology. 236(4). 2850–2868. 10 indexed citations
7.
Chakraborty, Supriyo, et al.. (2020). Analysis of codon usage bias of chloroplast genes in Oryza species. Planta. 252(4). 67–67. 61 indexed citations
8.
Deb, Bornali, Arif Uddin, & Supriyo Chakraborty. (2020). Codon usage pattern and its influencing factors in different genomes of hepadnaviruses. Archives of Virology. 165(3). 557–570. 45 indexed citations
9.
Chakraborty, Supriyo, et al.. (2020). A Crosstalk on Codon Usage in Genes Associated with Leukemia. Biochemical Genetics. 59(1). 235–255. 3 indexed citations
10.
Chakraborty, Supriyo, et al.. (2019). Analysis of codon usage patterns and influencing factors in Nipah virus. Virus Research. 263. 129–138. 35 indexed citations
11.
12.
Chakraborty, Supriyo, et al.. (2018). Compositional dynamics and codon usage pattern of BRCA1 gene across nine mammalian species. Genomics. 111(2). 167–176. 23 indexed citations
13.
Uddin, Arif, et al.. (2018). Compositional properties and codon usage of TP73 gene family. Gene. 683. 159–168. 16 indexed citations
14.
Uddin, Arif, et al.. (2017). Codon usage bias and its influencing factors for Y-linked genes in human. Computational Biology and Chemistry. 69. 77–86. 29 indexed citations
15.
Deb, Bornali, et al.. (2017). Analysis of codon usage pattern of mitochondrial protein-coding genes in different hookworms. Molecular and Biochemical Parasitology. 219. 24–32. 18 indexed citations
16.
Uddin, Arif, et al.. (2017). Codon usage pattern of complex III gene of respiratory chain among platyhelminths. Infection Genetics and Evolution. 57. 128–137. 8 indexed citations
17.
Uddin, Arif, et al.. (2017). Comparative analysis of codon usage pattern and its influencing factors in Schistosoma japonicum and Ascaris suum. Acta Parasitologica. 62(4). 748–761. 8 indexed citations
18.
Uddin, Arif & Supriyo Chakraborty. (2016). CODON USAGE IN HUMAN MITOCHONDRIAL GENES IN THE CONTEXT OF CANCER. International Journal of Pharmacy and Pharmaceutical Sciences. 8(13). 37–40. 2 indexed citations
19.
20.
Uddin, Arif, et al.. (2015). Codon bias and gene expression of mitochondrial ND2 gene in chordates. Bioinformation. 11(8). 407–412. 17 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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