Robert Dunne

1.7k total citations
24 papers, 1.2k citations indexed

About

Robert Dunne is a scholar working on Molecular Biology, Genetics and Artificial Intelligence. According to data from OpenAlex, Robert Dunne has authored 24 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 7 papers in Genetics and 6 papers in Artificial Intelligence. Recurrent topics in Robert Dunne's work include Genetic factors in colorectal cancer (4 papers), Bioinformatics and Genomic Networks (3 papers) and Remote-Sensing Image Classification (3 papers). Robert Dunne is often cited by papers focused on Genetic factors in colorectal cancer (4 papers), Bioinformatics and Genomic Networks (3 papers) and Remote-Sensing Image Classification (3 papers). Robert Dunne collaborates with scholars based in Australia, China and United States. Robert Dunne's co-authors include Ryan Lagerstrom, Andreas Ernst, J.F. Huntington, Harri Kiiveri, Mark Berman, Peter L. Molloy, Lawrence C. LaPointe, Graeme P. Young, Susanne K. Pedersen and Thu Ho and has published in prestigious journals such as PLoS ONE, The Journal of Clinical Endocrinology & Metabolism and Scientific Reports.

In The Last Decade

Robert Dunne

23 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert Dunne Australia 12 443 311 289 185 133 24 1.2k
Xiaodong Zhang China 25 975 2.2× 294 0.9× 170 0.6× 186 1.0× 55 0.4× 71 1.9k
Junjie Zhu China 20 936 2.1× 258 0.8× 45 0.2× 205 1.1× 45 0.3× 78 1.8k
Fei Feng China 22 424 1.0× 83 0.3× 158 0.5× 106 0.6× 77 0.6× 79 1.7k
Kai Song China 23 614 1.4× 162 0.5× 28 0.1× 272 1.5× 29 0.2× 64 1.5k
Wenwen Qi China 21 464 1.0× 114 0.4× 34 0.1× 111 0.6× 218 1.6× 78 1.4k
Xiuyuan Zhang China 15 411 0.9× 106 0.3× 190 0.7× 66 0.4× 121 0.9× 35 878
Monica Franzese Italy 22 533 1.2× 224 0.7× 48 0.2× 110 0.6× 24 0.2× 83 1.3k
Jiayi Guo China 18 476 1.1× 194 0.6× 276 1.0× 109 0.6× 22 0.2× 69 1.6k
Zhenglin Wang China 22 327 0.7× 200 0.6× 37 0.1× 216 1.2× 74 0.6× 102 1.8k
Weichen Zhang China 19 179 0.4× 166 0.5× 21 0.1× 88 0.5× 30 0.2× 97 1.3k

Countries citing papers authored by Robert Dunne

Since Specialization
Citations

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

Fields of papers citing papers by Robert Dunne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert Dunne

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Dunne. A scholar is included among the top collaborators of Robert Dunne 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 Robert Dunne. Robert Dunne 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.
Alexánder, David, et al.. (2024). Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications. Applied Sciences. 14(2). 496–496. 28 indexed citations
2.
Dunne, Robert, et al.. (2023). Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach. Computational and Structural Biotechnology Journal. 21. 4354–4360. 10 indexed citations
3.
Lundberg, Mischa, et al.. (2023). Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform. Scientific Reports. 13(1). 17662–17662. 6 indexed citations
4.
O’Brien, Aidan R., et al.. (2020). VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data. GigaScience. 9(8). 11 indexed citations
5.
Twine, Natalie A., et al.. (2018). High Activity Target-Site Identification Using Phenotypic Independent CRISPR-Cas9 Core Functionality. The CRISPR Journal. 1(2). 182–190. 40 indexed citations
6.
O’Brien, Aidan R., et al.. (2017). Breaking the Curse of Dimensionality for Machine Learning on Genomic Data.. International Joint Conference on Artificial Intelligence. 15–20. 1 indexed citations
7.
Patten, Glen S., Caroline A Kerr, Robert Dunne, et al.. (2015). Resistant Starch Alters Colonic Contractility and Expression of Related Genes in Rats Fed a Western Diet. Digestive Diseases and Sciences. 60(6). 1624–1632. 10 indexed citations
8.
Mitchell, Susan M., Jason P. Ross, Horace R. Drew, et al.. (2014). A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer. 14(1). 54–54. 131 indexed citations
9.
Kerr, Caroline A, Barney M. Hines, Janet M. Shaw, et al.. (2013). Genomic homeostasis is dysregulated in favour of apoptosis in the colonic epithelium of the azoxymethane treated rat. BMC Physiology. 13(1). 2–2. 10 indexed citations
10.
Conlon, Michael A., Caroline A Kerr, Christopher S. McSweeney, et al.. (2012). Resistant Starches Protect against Colonic DNA Damage and Alter Microbiota and Gene Expression in Rats Fed a Western Diet. Journal of Nutrition. 142(5). 832–840. 92 indexed citations
11.
LaPointe, Lawrence C., Susanne K. Pedersen, Robert Dunne, et al.. (2012). Discovery and Validation of Molecular Biomarkers for Colorectal Adenomas and Cancer with Application to Blood Testing. PLoS ONE. 7(1). e29059–e29059. 35 indexed citations
12.
Graham, Lloyd D., Susanne K. Pedersen, Gabrielle Brown, et al.. (2011). Colorectal Neoplasia Differentially Expressed (CRNDE), a Novel Gene with Elevated Expression in Colorectal Adenomas and Adenocarcinomas. Genes & Cancer. 2(8). 829–840. 220 indexed citations
13.
Dunne, Robert, et al.. (2011). Optimizing Decision Preparedness by Adapting Scenario Complexity and Automating Scenario Generation. NASA Technical Reports Server (NASA).
14.
Kerr, Caroline A, Robert Dunne, Barney M. Hines, et al.. (2009). Measuring the combinatorial expression of solute transporters and metalloproteinases transcripts in colorectal cancer. BMC Research Notes. 2(1). 164–164. 8 indexed citations
15.
Howell, Viive M., Anthony J. Gill, Adele Clarkson, et al.. (2008). Accuracy of Combined Protein Gene Product 9.5 and Parafibromin Markers for Immunohistochemical Diagnosis of Parathyroid Carcinoma. The Journal of Clinical Endocrinology & Metabolism. 94(2). 434–441. 97 indexed citations
16.
Kerr, Caroline A, Barney M. Hines, Si Ying Tan, et al.. (2008). Proximal and distal gene expression in the colonic epithelium azoxymethanetreated rats: inferences to IBD.. Inflammatory Bowel Diseases. 14. S35–S35. 1 indexed citations
17.
Dunne, Robert. (2007). A Statistical Approach to Neural Networks for Pattern Recognition (Wiley Series in Computational Statistics). Wiley-Interscience eBooks. 4 indexed citations
18.
LaPointe, Lawrence C., Robert Dunne, Daniel L. Worthley, et al.. (2007). Map of differential transcript expression in the normal human large intestine. Physiological Genomics. 33(1). 50–64. 73 indexed citations
19.
Dunne, Robert. (2006). A Statistical Approach to Neural Networks for Pattern Recognition. CERN Document Server (European Organization for Nuclear Research). 30 indexed citations
20.
Casu, Rosanne E., John M. Manners, Graham D. Bonnett, et al.. (2005). Genomics approaches for the identification of genes determining important traits in sugarcane. Field Crops Research. 92(2-3). 137–147. 44 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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