Ryan J. Farr

855 total citations
21 papers, 542 citations indexed

About

Ryan J. Farr is a scholar working on Molecular Biology, Cancer Research and Surgery. According to data from OpenAlex, Ryan J. Farr has authored 21 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 11 papers in Cancer Research and 4 papers in Surgery. Recurrent topics in Ryan J. Farr's work include MicroRNA in disease regulation (10 papers), Extracellular vesicles in disease (4 papers) and Advanced biosensing and bioanalysis techniques (4 papers). Ryan J. Farr is often cited by papers focused on MicroRNA in disease regulation (10 papers), Extracellular vesicles in disease (4 papers) and Advanced biosensing and bioanalysis techniques (4 papers). Ryan J. Farr collaborates with scholars based in Australia, Spain and United Kingdom. Ryan J. Farr's co-authors include Mugdha V. Joglekar, Anandwardhan A. Hardikar, Cameron R. Stewart, Christopher Cowled, Megan Dearnley, Emily Kerr, Andrew G. D. Bean, Leon Tribolet, Andrzej S. Januszewski and Christina L. Rootes and has published in prestigious journals such as PLoS ONE, Diabetes and Scientific Reports.

In The Last Decade

Ryan J. Farr

19 papers receiving 535 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ryan J. Farr Australia 11 330 271 119 56 50 21 542
Yao Lei China 12 386 1.2× 199 0.7× 106 0.9× 33 0.6× 78 1.6× 33 665
Ji Hoon Jeon South Korea 13 198 0.6× 64 0.2× 206 1.7× 58 1.0× 82 1.6× 26 561
Sandeep Goswami India 15 245 0.7× 41 0.2× 118 1.0× 34 0.6× 57 1.1× 34 528
Javier T. Granados-Riverón Mexico 8 372 1.1× 191 0.7× 67 0.6× 8 0.1× 25 0.5× 19 458
Sabari Nath Neerukonda United States 13 191 0.6× 63 0.2× 224 1.9× 14 0.3× 86 1.7× 22 487
Christoph K. Stein‐Thoeringer Germany 11 447 1.4× 51 0.2× 99 0.8× 77 1.4× 49 1.0× 21 694
Franziska Schmidt Germany 9 163 0.5× 118 0.4× 104 0.9× 19 0.3× 27 0.5× 27 349
Xiwen Gao China 11 251 0.8× 130 0.5× 28 0.2× 17 0.3× 35 0.7× 30 530
Jerzy Kołodziej Poland 9 128 0.4× 56 0.2× 63 0.5× 49 0.9× 25 0.5× 29 405
Yi Ru China 14 140 0.4× 74 0.3× 114 1.0× 23 0.4× 59 1.2× 30 463

Countries citing papers authored by Ryan J. Farr

Since Specialization
Citations

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

Fields of papers citing papers by Ryan J. Farr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryan J. Farr. 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 Ryan J. Farr. The network helps show where Ryan J. Farr may publish in the future.

Co-authorship network of co-authors of Ryan J. Farr

This figure shows the co-authorship network connecting the top 25 collaborators of Ryan J. Farr. A scholar is included among the top collaborators of Ryan J. Farr 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 Ryan J. Farr. Ryan J. Farr 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.
Legrand, Thibault P. R. A., et al.. (2025). Genome-centric metagenomics reveals uncharacterised microbiomes in Angus cattle. Scientific Data. 12(1). 547–547.
3.
Alexandre, Pâmela A., Brad C. Hine, Aaron Ingham, et al.. (2025). Single-cell transcriptomics uncovers key immune drivers of vaccine efficacy in cattle. BMC Genomics. 26(1). 750–750. 1 indexed citations
4.
Wong, Wilson K. M., et al.. (2023). MicroRNA Profiling from Tears as a Potential Non-invasive Method for Early Detection of Diabetic Retinopathy. Methods in molecular biology. 2678. 117–134. 3 indexed citations
5.
Farr, Ryan J., Christina L. Rootes, John Stenos, et al.. (2022). Detection of SARS-CoV-2 infection by microRNA profiling of the upper respiratory tract. PLoS ONE. 17(4). e0265670–e0265670. 23 indexed citations
6.
Farr, Ryan J., Christina L. Rootes, Louise C. Rowntree, et al.. (2021). Altered microRNA expression in COVID-19 patients enables identification of SARS-CoV-2 infection. PLoS Pathogens. 17(7). e1009759–e1009759. 115 indexed citations
7.
Januszewski, Andrzej S., Yoon Hi Cho, Mugdha V. Joglekar, et al.. (2021). Insulin micro-secretion in Type 1 diabetes and related microRNA profiles. Scientific Reports. 11(1). 11727–11727. 20 indexed citations
8.
Farr, Ryan J., Nathan Gödde, Christopher Cowled, et al.. (2021). Machine Learning Identifies Cellular and Exosomal MicroRNA Signatures of Lyssavirus Infection in Human Stem Cell-Derived Neurons. Frontiers in Cellular and Infection Microbiology. 11. 783140–783140. 8 indexed citations
9.
Kerr, Emily, Luke C. Henderson, David J. Hayne, et al.. (2021). Amplification-Free Electrochemiluminescence Molecular Beacon-Based microRNA Sensing Using a Mobile Phone for Detection. ECS Meeting Abstracts. MA2021-01(61). 1618–1618. 6 indexed citations
10.
Tribolet, Leon, Emily Kerr, Christopher Cowled, et al.. (2020). MicroRNA Biomarkers for Infectious Diseases: From Basic Research to Biosensing. Frontiers in Microbiology. 11. 1197–1197. 141 indexed citations
11.
Kerr, Emily, Ryan J. Farr, Egan H. Doeven, et al.. (2020). Amplification-free electrochemiluminescence molecular beacon-based microRNA sensing using a mobile phone for detection. Sensors and Actuators B Chemical. 330. 129261–129261. 38 indexed citations
12.
Sundaramoorthy, Vinod, Nathan Gödde, Ryan J. Farr, et al.. (2020). Modelling Lyssavirus Infections in Human Stem Cell-Derived Neural Cultures. Viruses. 12(4). 359–359. 15 indexed citations
13.
Farr, Ryan J., Wilson K. M. Wong, Sarah A. Tersey, et al.. (2019). Comparative analysis of diagnostic platforms for measurement of differentially methylated insulin DNA. Journal of Biological Methods. 6(2). 1–1. 2 indexed citations
14.
Januszewski, Andrzej S., Mugdha V. Joglekar, Luke Carroll, et al.. (2019). 38-LB: Discovery Analysis of MicroRNAs (miRs) Associated with Microvascular Complications in Adults with Type 1 Diabetes. Diabetes. 68(Supplement_1). 1 indexed citations
15.
Wong, Wilson K. M., Ryan J. Farr, Mugdha V. Joglekar, Andrzej S. Januszewski, & Anandwardhan A. Hardikar. (2015). Probe-based Real-time PCR Approaches for Quantitative Measurement of microRNAs. Journal of Visualized Experiments. 24 indexed citations
16.
Farr, Ryan J., Mugdha V. Joglekar, & Anandwardhan A. Hardikar. (2015). Circulating microRNAs in Diabetes Progression: Discovery, Validation, and Research Translation. Proceedings of the Fourth International Symposium on Polarization Phenomena in Nuclear Reactions. 106. 215–244. 11 indexed citations
17.
Farr, Ryan J., Andrzej S. Januszewski, Mugdha V. Joglekar, et al.. (2015). A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy. Scientific Reports. 5(1). 10375–10375. 54 indexed citations
18.
Hardikar, Anandwardhan A., Ryan J. Farr, & Mugdha V. Joglekar. (2014). Circulating microRNAs: Understanding the Limits for Quantitative Measurement by Real‐Time PCR. Journal of the American Heart Association. 3(1). e000792–e000792. 45 indexed citations
19.
20.
Farr, Ryan J., Mugdha V. Joglekar, Caroline J. Taylor, & Anandwardhan A. Hardikar. (2013). Circulating non-coding RNAs as biomarkers of beta cell death in diabetes.. PubMed. 11(1). 14–20. 26 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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