William Broomall
Impact in
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- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Metabolomics and Mass Spectrometry Studies
- Genomics and Phylogenetic Studies
- Machine Learning in Bioinformatics
Papers in
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- Bioinformatics and Genomic Networks 9
- Genomics and Phylogenetic Studies 5
- Machine Learning in Bioinformatics 5
- Biomedical Text Mining and Ontologies 3
- Gene expression and cancer classification 2
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- Advanced Proteomics Techniques and Applications 4
- Co-authors
- Natali Kolker (12 shared papers)Roger Higdon (13 shared papers)Eugene Kolker (12 shared papers)Larissa Stanberry (11 shared papers)Winston Haynes (7 shared papers)Doron Lancet (2 shared papers)Elizabeth Stewart (3 shared papers)Caitlin M. Hudac (1 shared paper)
- Journals
- OMICS A Journal of Integrative Biology (4 papers)Nucleic Acids Research (2 papers)Journal of Proteomics (1 paper)Journal of Proteome Research (1 paper)Big Data (1 paper)
- Partner nations
- United StatesMexicoFinland
In The Last Decade
William Broomall
13 papers receiving 315 citations
Peers
Comparison fields: 5 of 94
- Aging 7
- Molecular Biology 207
- Spectroscopy 49
- Genetics 49
- Information Systems and Management 11
Countries citing papers authored by William Broomall
This map shows the geographic impact of William Broomall'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 William Broomall with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Broomall more than expected).
Fields of papers citing papers by William Broomall
This network shows the impact of papers produced by William Broomall. 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 William Broomall. The network helps show where William Broomall may publish in the future.
Co-authors
The 24 scholars most cited alongside William Broomall, 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 | 2011 | 95 | |
| 2 | 2015 | 72 | |
| 3 | 2014 | 44 | |
| 4 | 2012 | 34 | |
| 5 | 2011 | 24 | |
| 6 | 2013 | 17 | |
| 7 | 2014 | 15 | |
| 8 | 2011 | 12 | |
| 9 | 2013 | 4 | |
| 10 | 2014 | 3 | |
| 11 | 2015 | 3 | |
| 12 | 2012 | 3 | |
| 13 | 2013 | 1 |
About William Broomall
William Broomall is a scholar working on Molecular Biology, Spectroscopy, Artificial Intelligence, Genetics and Cognitive Neuroscience, having authored 13 papers that have together received 327 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (9 papers), Genomics and Phylogenetic Studies (5 papers), Machine Learning in Bioinformatics (5 papers), Advanced Proteomics Techniques and Applications (4 papers), Biomedical Text Mining and Ontologies (3 papers), Gene expression and cancer classification (2 papers), Genomics and Rare Diseases (2 papers) and Algorithms and Data Compression (2 papers). The work is most often cited by research in Aging (7 citations), Molecular Biology (207 citations), Spectroscopy (49 citations), Genetics (49 citations) and Information Systems and Management (11 citations). William Broomall has collaborated with scholars based in United States, Mexico and Finland. Frequent co-authors include Natali Kolker, Roger Higdon, Eugene Kolker, Larissa Stanberry, Winston Haynes, Doron Lancet, Elizabeth Stewart, Caitlin M. Hudac, Rachel K. Earl and Raphael Bernier. Their work appears in journals such as OMICS A Journal of Integrative Biology, Nucleic Acids Research, Journal of Proteomics, Journal of Proteome Research and Big Data.
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.