Chris Burge
Impact in
- Molecular Biology top 2%
- Genomics and Phylogenetic Studies
- RNA and protein synthesis mechanisms
- Machine Learning in Bioinformatics
- RNA Research and Splicing
- Fractal and DNA sequence analysis
- Genomics and Chromatin Dynamics
- RNA modifications and cancer
- Plant Science top 2%
- Chromosomal and Genetic Variations
Papers in
-
- Genomics and Phylogenetic Studies 4
- RNA and protein synthesis mechanisms 4
- Machine Learning in Bioinformatics 2
- Genomics and Chromatin Dynamics 1
- DNA Repair Mechanisms 1
- Fractal and DNA sequence analysis 1
- DNA and Nucleic Acid Chemistry 1
- Ecology 1
- Co-authors
- Samuel Karlin (5 shared papers)B. Edwin Blaisdell (3 shared papers)Ming‐Ying Leung (1 shared paper)Lon R. Cardon (2 shared papers)Ronald J. Sapolsky (1 shared paper)Reinhard Engels (1 shared paper)Jill P. Mesirov (1 shared paper)David DeCaprio (1 shared paper)
- Journals
- Nucleic Acids Research (3 papers)Journal of Molecular Biology (2 papers)Trends in Genetics (1 paper)Bioinformatics (1 paper)
- Partner nations
- United States
In The Last Decade
Chris Burge
7 papers receiving 3.7k citations
Chris Burge's Hit Papers
Peers
Comparison fields: 5 of 129
- Molecular Biology 2.7k
- Plant Science 868
- Genetics 591
- Horticulture 14
- Immunology 268
Countries citing papers authored by Chris Burge
This map shows the geographic impact of Chris Burge'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 Chris Burge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Burge more than expected).
Fields of papers citing papers by Chris Burge
This network shows the impact of papers produced by Chris Burge. 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 Chris Burge. The network helps show where Chris Burge may publish in the future.
Co-authors
The 10 scholars most cited alongside Chris Burge, 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 | Prediction of complete gene structures in human genomic DNA Hit paper breakdown → | 1997 | 3092 |
| 2 | Dinucleotide relative abundance extremes: a genomic signature Hit paper breakdown → | 1995 | 535 |
| 3 | 1992 | 97 | |
| 4 | 1991 | 46 | |
| 5 | 1993 | 39 | |
| 6 | 2006 | 33 | |
| 7 | 1993 | 12 |
About Chris Burge
Chris Burge is a scholar working on Molecular Biology, Ecology, Artificial Intelligence, Genetics and Plant Science, having authored 7 papers that have together received 3.9k indexed citations. Recurring topics across this work include Genomics and Phylogenetic Studies (4 papers), RNA and protein synthesis mechanisms (4 papers), Machine Learning in Bioinformatics (2 papers), Genomics and Chromatin Dynamics (1 paper), DNA Repair Mechanisms (1 paper), Fractal and DNA sequence analysis (1 paper), DNA and Nucleic Acid Chemistry (1 paper) and Bacterial Genetics and Biotechnology (1 paper). The work is most often cited by research in Molecular Biology (2.7k citations), Plant Science (868 citations), Genetics (591 citations), Horticulture (14 citations) and Immunology (268 citations). Chris Burge has collaborated with scholars based in United States. Frequent co-authors include Samuel Karlin, B. Edwin Blaisdell, Ming‐Ying Leung, Lon R. Cardon, Ronald J. Sapolsky, Reinhard Engels, Jill P. Mesirov, David DeCaprio, James E. Galagan and Gabriel Schachtel. Their work appears in journals such as Nucleic Acids Research, Journal of Molecular Biology, Trends in Genetics 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.