Len Trigg
Impact in
- Cancer Research top 10%
- Cancer Genomics and Diagnostics
- Molecular Biology top 10%
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
Papers in
- Genetics 3
- Genomics and Rare Diseases 3
- Genetic Associations and Epidemiology 2
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- Genomics and Phylogenetic Studies 4
- Gene expression and cancer classification 2
- Microbial Metabolic Engineering and Bioproduction 1
- Metabolomics and Mass Spectrometry Studies 1
- Machine Learning in Bioinformatics 1
- Co-authors
- Geoffrey HolmesEibe FrankMark HallIan H. WittenFrancisco M. De La VegaMarc SalitJustin M. ZookRebecca Truty
- Journals
- Nature Biotechnology (2 papers)Journal of Biomolecular Techniques JBT (1 paper)Journal of Computational Biology (1 paper)Bioinformatics (1 paper)
- Partner nations
- New ZealandUnited StatesCanada
In The Last Decade
Len Trigg
6 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 140
- Cancer Research 178
- Molecular Biology 735
- Genetics 259
- Computational Theory and Mathematics 100
- Health Information Management 17
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-authorship network
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 | 2024 | 0 | |
| 2 | 2019 | 182 | |
| 3 | 2019 | 152 | |
| 4 | 2014 | 52 | |
| 5 | Quantitative Analysis of Shotgun Metagenomic Data with the Real Time Genomics Platform | 2013 | 2 |
| 6 | 2006 | 0 | |
| 7 | Data mining in bioinformatics using Weka Hit paper breakdown → | 2004 | 712 |
| 8 | 2003 | 5 |
About Len Trigg
Len Trigg is a scholar working on Genetics, Molecular Biology, Artificial Intelligence, Pharmacology and Food Science, having authored 8 papers that have together received 1.1k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (4 papers), Genomics and Rare Diseases (3 papers), Genetic Associations and Epidemiology (2 papers), Gene expression and cancer classification (2 papers), Microbial Metabolic Engineering and Bioproduction (1 paper), Metabolomics and Mass Spectrometry Studies (1 paper), Machine Learning in Bioinformatics (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Cancer Research (178 citations), Molecular Biology (735 citations), Genetics (259 citations), Computational Theory and Mathematics (100 citations) and Health Information Management (17 citations). Len Trigg has collaborated with scholars based in New Zealand, United States and Canada. Frequent co-authors include Geoffrey Holmes, Eibe Frank, Mark Hall, Ian H. Witten, Francisco M. De La Vega, Marc Salit, Justin M. Zook, Rebecca Truty, Sean A. Irvine and Jennifer McDaniel. Their work appears in journals such as Nature Biotechnology, Journal of Biomolecular Techniques JBT, Journal of Computational Biology 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.