Thiruvarangan Ramaraj

2.7k total citations
49 papers, 1.5k citations indexed

About

Thiruvarangan Ramaraj is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Thiruvarangan Ramaraj has authored 49 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 19 papers in Plant Science and 5 papers in Genetics. Recurrent topics in Thiruvarangan Ramaraj's work include Genomics and Phylogenetic Studies (15 papers), Chromosomal and Genetic Variations (9 papers) and Plant Virus Research Studies (6 papers). Thiruvarangan Ramaraj is often cited by papers focused on Genomics and Phylogenetic Studies (15 papers), Chromosomal and Genetic Variations (9 papers) and Plant Virus Research Studies (6 papers). Thiruvarangan Ramaraj collaborates with scholars based in United States, China and United Kingdom. Thiruvarangan Ramaraj's co-authors include Joann Mudge, Eric N. Jellen, Peter J. Maughan, Brendan Mumey, Li Luo, Vallabh O. Shah, Callum J. Bell, Jonathan W. Leff, Algirdas J. Jesaitis and Anitha Sundararajan and has published in prestigious journals such as PLoS ONE, Molecular and Cellular Biology and Scientific Reports.

In The Last Decade

Thiruvarangan Ramaraj

47 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thiruvarangan Ramaraj United States 20 798 690 170 145 141 49 1.5k
Rohan G. T. Lowe Australia 19 805 1.0× 1.0k 1.5× 119 0.7× 52 0.4× 48 0.3× 34 2.0k
Xiaoyan Cui China 24 773 1.0× 823 1.2× 177 1.0× 49 0.3× 70 0.5× 83 2.0k
Pradhyumna Kumar Singh India 25 1.4k 1.7× 932 1.4× 120 0.7× 69 0.5× 32 0.2× 65 2.1k
Johannes Madlung Germany 25 1.1k 1.3× 623 0.9× 178 1.0× 62 0.4× 155 1.1× 35 2.0k
Bing Dong China 25 855 1.1× 264 0.4× 127 0.7× 281 1.9× 289 2.0× 66 2.1k
Xiaoling Gao China 25 686 0.9× 633 0.9× 76 0.4× 60 0.4× 93 0.7× 101 1.9k
Yannick Lippi France 22 480 0.6× 788 1.1× 73 0.4× 117 0.8× 123 0.9× 43 1.4k
Walter Sanseverino Italy 30 1.1k 1.3× 1.5k 2.1× 354 2.1× 141 1.0× 41 0.3× 69 2.4k
David Baker United Kingdom 26 1.3k 1.6× 858 1.2× 270 1.6× 145 1.0× 100 0.7× 69 2.2k
David Cohen France 18 1.2k 1.5× 231 0.3× 360 2.1× 282 1.9× 126 0.9× 46 1.9k

Countries citing papers authored by Thiruvarangan Ramaraj

Since Specialization
Citations

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

Fields of papers citing papers by Thiruvarangan Ramaraj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thiruvarangan Ramaraj

This figure shows the co-authorship network connecting the top 25 collaborators of Thiruvarangan Ramaraj. A scholar is included among the top collaborators of Thiruvarangan Ramaraj 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 Thiruvarangan Ramaraj. Thiruvarangan Ramaraj 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.
Wang, Yiyang, et al.. (2023). Optimizing Computer-Aided Diagnosis with Cost-Aware Deep Learning Models. PubMed. 29. 108–119. 2 indexed citations
3.
Grover, Corrinne E., Mark A. Arick, Adam Thrash, et al.. (2022). Dual Domestication, Diversity, and Differential Introgression in Old World Cotton Diploids. Genome Biology and Evolution. 14(12). 11 indexed citations
4.
Ramaraj, Thiruvarangan, Corrinne E. Grover, Mark A. Arick, et al.. (2022). The Gossypium herbaceum L. Wagad genome as a resource for understanding cotton domestication. G3 Genes Genomes Genetics. 13(2). 3 indexed citations
5.
Udall, Joshua A., Evan Long, Daojun Yuan, et al.. (2019). De Novo Genome Sequence Assemblies of Gossypium raimondii and Gossypium turneri. G3 Genes Genomes Genetics. 9(10). 3079–3085. 68 indexed citations
6.
Li, Guangyuan, Anitha Sundararajan, Thiruvarangan Ramaraj, et al.. (2018). Histone Citrullination Represses MicroRNA Expression, Resulting in Increased Oncogene mRNAs in Somatolactotrope Cells. Molecular and Cellular Biology. 38(19). 23 indexed citations
7.
Sundararajan, Anitha, Hallie S. Rane, Thiruvarangan Ramaraj, et al.. (2018). Cranberry-derived proanthocyanidins induce a differential transcriptomic response within Candida albicans urinary biofilms. PLoS ONE. 13(8). e0201969–e0201969. 4 indexed citations
8.
Moll, Karen, Peng Zhou, Thiruvarangan Ramaraj, et al.. (2017). Strategies for optimizing BioNano and Dovetail explored through a second reference quality assembly for the legume model, Medicago truncatula. BMC Genomics. 18(1). 578–578. 39 indexed citations
9.
Miller, Jason, Peng Zhou, Joann Mudge, et al.. (2017). Hybrid assembly with long and short reads improves discovery of gene family expansions. BMC Genomics. 18(1). 541–541. 41 indexed citations
10.
Neupane, Durga, et al.. (2017). Zinc-Dependent Transcriptional Regulation in Paracoccus denitrificans. Frontiers in Microbiology. 8. 569–569. 16 indexed citations
11.
Zhou, Peng, Kevin A.T. Silverstein, Thiruvarangan Ramaraj, et al.. (2017). Exploring structural variation and gene family architecture with De Novo assemblies of 15 Medicago genomes. BMC Genomics. 18(1). 261–261. 67 indexed citations
12.
Adhikary, Dinesh, Justin T. Page, Thiruvarangan Ramaraj, et al.. (2016). The Amaranth Genome: Genome, Transcriptome, and Physical Map Assembly. The Plant Genome. 9(1). 133 indexed citations
13.
Chaney, Lindsay, et al.. (2016). The complete chloroplast genome sequences for four Amaranthus species (Amaranthaceae). Applications in Plant Sciences. 4(9). 42 indexed citations
14.
Dukowic‐Schulze, Stefanie, Anitha Sundararajan, Thiruvarangan Ramaraj, et al.. (2016). Novel Meiotic miRNAs and Indications for a Role of PhasiRNAs in Meiosis. Frontiers in Plant Science. 7. 762–762. 48 indexed citations
15.
Smith, Heidi J., et al.. (2016). Genome Sequence of Janthinobacterium sp. CG23_2, a Violacein-Producing Isolate from an Antarctic Supraglacial Stream. Genome Announcements. 4(1). 15 indexed citations
16.
Shah, Vallabh O., et al.. (2015). Composition Diversity and Abundance of Gut Microbiome in Prediabetes and Type 2 Diabetes. PubMed. 2(2). 108–114. 222 indexed citations
17.
Schirtzinger, Erin E., Nicholas P. Devitt, Thiruvarangan Ramaraj, et al.. (2014). Repertoire of virus-derived small RNAs produced by mosquito and mammalian cells in response to dengue virus infection. Virology. 476. 54–60. 11 indexed citations
18.
19.
Dukowic‐Schulze, Stefanie, Anitha Sundararajan, Thiruvarangan Ramaraj, Joann Mudge, & Changbin Chen. (2014). Sequencing-based large-scale genomics approaches with small numbers of isolated maize meiocytes. Frontiers in Plant Science. 5. 57–57. 16 indexed citations
20.
Dukowic‐Schulze, Stefanie, Anitha Sundararajan, Joann Mudge, et al.. (2014). The transcriptome landscape of early maize meiosis. BMC Plant Biology. 14(1). 118–118. 42 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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