Mark D. Robinson
Impact in
- Cancer Research top 0.05%
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Molecular Biology top 0.02%
- RNA modifications and cancer
- Single-cell and spatial transcriptomics
- RNA Research and Splicing
- Bioinformatics and Genomic Networks
- Epigenetics and DNA Methylation
- Gene expression and cancer classification
Papers in
-
- Single-cell and spatial transcriptomics 46
- Gene expression and cancer classification 31
- Epigenetics and DNA Methylation 25
- RNA Research and Splicing 23
- RNA modifications and cancer 20
- Bioinformatics and Genomic Networks 17
-
- Cancer-related molecular mechanisms research 17
- Co-authors
- Gordon Smyth (5 shared papers)Davis James McCarthy (3 shared papers)Alicia Oshlack (4 shared papers)Charlotte Soneson (33 shared papers)Michael I. Love (6 shared papers)Lukas M. Weber (15 shared papers)Małgorzata Nowicka (9 shared papers)Pierre‐Luc Germain (12 shared papers)
- Journals
- Genome biology (18 papers)F1000Research (17 papers)Nature Communications (9 papers)BMC Genomics (7 papers)Bioinformatics (6 papers)
- Partner nations
- SwitzerlandUnited StatesAustralia
In The Last Decade
Mark D. Robinson
214 papers receiving 70.1k citations
Mark D. Robinson's Hit Papers
Peers
Comparison fields: 5 of 218
- Cancer Research 9.8k
- Molecular Biology 39.6k
- Aging 696
- Immunology 7.9k
- Genetics 3.2k
Countries citing papers authored by Mark D. Robinson
This map shows the geographic impact of Mark D. Robinson'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 Mark D. Robinson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark D. Robinson more than expected).
Fields of papers citing papers by Mark D. Robinson
This network shows the impact of papers produced by Mark D. Robinson. 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 Mark D. Robinson. The network helps show where Mark D. Robinson may publish in the future.
Co-authors
The 25 scholars most cited alongside Mark D. Robinson, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 223 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | edgeR : a Bioconductor package for differential expression analysis of digital gene expression data Hit paper breakdown → | 2009 | 31219 |
| 2 | Comprehensive genomic characterization defines human glioblastoma genes and core pathways Hit paper breakdown → | 2008 | 6384 |
| 3 | A scaling normalization method for differential expression analysis of RNA-seq data Hit paper breakdown → | 2010 | 5691 |
| 4 | Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences Hit paper breakdown → | 2015 | 2874 |
| 5 | Global landscape of protein complexes in the yeast Saccharomyces cerevisiae Hit paper breakdown → | 2006 | 2338 |
| 6 | Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants Hit paper breakdown → | 2001 | 1676 |
| 7 | Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences Hit paper breakdown → | 2016 | 971 |
| 8 | Count-based differential expression analysis of RNA sequencing data using R and Bioconductor Hit paper breakdown → | 2013 | 894 |
| 9 | Eleven grand challenges in single-cell data science Hit paper breakdown → | 2020 | 831 |
| 10 | Small-sample estimation of negative binomial dispersion, with applications to SAGE data Hit paper breakdown → | 2007 | 809 |
| 11 | Large‐scale mapping of human protein–protein interactions by mass spectrometry Hit paper breakdown → | 2007 | 800 |
| 12 | 2007 | 632 | |
| 13 | Doublet identification in single-cell sequencing data using scDblFinder Hit paper breakdown → | 2021 | 596 |
| 14 | 2005 | 593 | |
| 15 | High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy Hit paper breakdown → | 2018 | 580 |
| 16 | 2010 | 528 | |
| 17 | Bias, robustness and scalability in single-cell differential expression analysis Hit paper breakdown → | 2018 | 474 |
| 18 | 2004 | 360 | |
| 19 | 2018 | 353 | |
| 20 | 2017 | 340 |
About Mark D. Robinson
Mark D. Robinson is a scholar working on Molecular Biology, Cancer Research, Biophysics, Immunology and Genetics, having authored 223 papers that have together received 70.9k indexed citations. Recurring topics across this work include Single-cell and spatial transcriptomics (46 papers), Gene expression and cancer classification (31 papers), Epigenetics and DNA Methylation (25 papers), RNA Research and Splicing (23 papers), RNA modifications and cancer (20 papers), Cancer-related molecular mechanisms research (17 papers), Bioinformatics and Genomic Networks (17 papers) and Cell Image Analysis Techniques (15 papers). The work is most often cited by research in Cancer Research (9.8k citations), Molecular Biology (39.6k citations), Aging (696 citations), Immunology (7.9k citations) and Genetics (3.2k citations). Mark D. Robinson has collaborated with scholars based in Switzerland, United States and Australia. Frequent co-authors include Gordon Smyth, Davis James McCarthy, Alicia Oshlack, Charlotte Soneson, Michael I. Love, Lukas M. Weber, Małgorzata Nowicka, Pierre‐Luc Germain, Timothy R. Hughes and Helen A. Lindsay. Their work appears in journals such as Genome biology, F1000Research, Nature Communications, BMC Genomics and Bioinformatics.
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.