Len Trigg
Impact in
- Molecular Biology top 10%
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- Gene expression and cancer classification
- Genetics top 10%
- Genomics and Rare Diseases
- Genomic variations and chromosomal abnormalities
Papers in
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- Genomics and Phylogenetic Studies 3
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- Data Mining Algorithms and Applications 1
- Co-authors
- Geoffrey Holmes (5 shared papers)Ian H. Witten (3 shared papers)Eibe Frank (3 shared papers)Richard Kirkby (2 shared papers)Bernhard Pfahringer (2 shared papers)Francisco M. De La Vega (3 shared papers)Marc Salit (2 shared papers)Justin M. Zook (2 shared papers)
- Journals
- Nature Biotechnology (2 papers)Journal of Biomolecular Techniques JBT (1 paper)Bioinformatics (1 paper)Genome biology (1 paper)Journal of Computational Biology (1 paper)
- Partner nations
- New ZealandUnited StatesUnited Kingdom
In The Last Decade
Len Trigg
10 papers receiving 1.7k citations
Len Trigg's Hit Papers
Peers
Comparison fields: 5 of 113
- Molecular Biology 546
- Genetics 166
- Computational Theory and Mathematics 77
- Health Information Management 25
- Information Systems 84
Countries citing papers authored by Len Trigg
This map shows the geographic impact of Len Trigg'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 Len Trigg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Len Trigg more than expected).
Fields of papers citing papers by Len Trigg
This network shows the impact of papers produced by Len Trigg. 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 Len Trigg. The network helps show where Len Trigg may publish in the future.
Co-authors
The 25 scholars most cited alongside Len Trigg, 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 | Data mining in bioinformatics using Weka Hit paper breakdown → | 2004 | 752 |
| 2 | 2009 | 357 | |
| 3 | Best practices for benchmarking germline small-variant calls in human genomes Hit paper breakdown → | 2019 | 255 |
| 4 | 2019 | 236 | |
| 5 | 2006 | 99 | |
| 6 | 2014 | 63 | |
| 7 | 2022 | 28 | |
| 8 | 2003 | 6 | |
| 9 | 2006 | 4 | |
| 10 | Quantitative Analysis of Shotgun Metagenomic Data with the Real Time Genomics Platform | 2013 | 2 |
About Len Trigg
Len Trigg is a scholar working on Molecular Biology, Information Systems, Plant Science, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 10 papers that have together received 1.8k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (3 papers), Data Mining Algorithms and Applications (1 paper) and Smart Agriculture and AI (1 paper). The work is most often cited by research in Molecular Biology (546 citations), Genetics (166 citations), Computational Theory and Mathematics (77 citations), Health Information Management (25 citations) and Information Systems (84 citations). Len Trigg has collaborated with scholars based in New Zealand, United States and United Kingdom. Frequent co-authors include Geoffrey Holmes, Ian H. Witten, Eibe Frank, Richard Kirkby, Bernhard Pfahringer, Francisco M. De La Vega, Marc Salit, Justin M. Zook, Rebecca Truty and Sean A. Irvine. Their work appears in journals such as Nature Biotechnology, Journal of Biomolecular Techniques JBT, Bioinformatics, Genome biology and Journal of Computational Biology.
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.