Martin Krzywinski
- Molecular Biology top 0.5%
- Genomics and Phylogenetic Studies 7
- Gene expression and cancer classification 6
- Plant Science top 0.5%
- Health Informatics top 1%
- Horticulture top 2%
- Genetics top 1%
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- Statistical Methods and Applications 10
- Advanced Statistical Methods and Models 7
- Statistical Methods in Clinical Trials 5
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- Time Series Analysis and Forecasting 6
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- Meta-analysis and systematic reviews 6
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- Data Visualization and Analytics 5
- Co-authors
- Naomi AltmanMarco A. MarraSteven J.M. Jonesİnanç BirolJacqueline E. ScheinRandy D. GascoyneJoseph M. ConnorsDoug Horsman
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
Martin Krzywinski
115 papers receiving 17.5k citations
Hit Papers
Peers
Comparison fields: 5 of 233
- Molecular Biology 7.3k
- Plant Science 3.6k
- Health Informatics 114
- Horticulture 64
- Genetics 1.8k
Countries citing papers authored by Martin Krzywinski
This map shows the geographic impact of Martin Krzywinski'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 Martin Krzywinski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Martin Krzywinski more than expected).
Fields of papers citing papers by Martin Krzywinski
This network shows the impact of papers produced by Martin Krzywinski. 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 Martin Krzywinski. The network helps show where Martin Krzywinski may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Martin Krzywinski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2023 | 9 | |
| 3 | 2022 | 15 | |
| 4 | 2022 | 4 | |
| 5 | 2021 | 32 | |
| 6 | 2020 | 128 | |
| 7 | 2018 | 83 | |
| 8 | 2018 | 6 | |
| 9 | 2017 | 5 | |
| 10 | 2016 | 10 | |
| 11 | 2016 | 9 | |
| 12 | Model selection and overfittingbreakdown → | 2016 | 469 |
| 13 | 2013 | 18 | |
| 14 | Fusobacterium nucleatum infection is prevalent in human colorectal carcinomabreakdown → | 2011 | 1480 |
| 15 | 2011 | 158 | |
| 16 | 2009 | 37 | |
| 17 | Circos: An information aesthetic for comparative genomicsbreakdown → | 2009 | 7765 |
| 18 | Sequencing the SARS virus | 2003 | 0 |
| 19 | 2003 | 19 | |
| 20 | Picking Cluster Parts: Cluster Construction at the Genome Sequence Centre. | 2001 | 1 |
About Martin Krzywinski
Martin Krzywinski is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Signal Processing, having authored 119 papers that have together received 17.8k indexed citations. Recurring topics across this work include Statistical Methods and Applications (10 papers), Genomics and Phylogenetic Studies (7 papers), Advanced Statistical Methods and Models (7 papers), Gene expression and cancer classification (6 papers), Time Series Analysis and Forecasting (6 papers), Meta-analysis and systematic reviews (6 papers), Data Visualization and Analytics (5 papers) and Statistical Methods in Clinical Trials (5 papers). The work is most often cited by research in Molecular Biology (7.3k citations), Plant Science (3.6k citations) and Health Informatics (114 citations). Martin Krzywinski has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include Naomi Altman, Marco A. Marra, Steven J.M. Jones, İnanç Birol, Jacqueline E. Schein, Randy D. Gascoyne, Joseph M. Connors, Doug Horsman, Jake Lever and Danilo Bzdok. Their work appears in journals such as Cell, Nucleic Acids Research and Nature Communications.
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.