Name
Abstract | ||
|---|---|---|
Transcribed exons in genes are joined together at donor and acceptor splice sites precisely and efficiently to generate mRNAs capable of being translated into proteins. The sequence variability in individual splice sites can be modeled using Shannon information theory. In the laboratory, the degree of individual splice site use is inferred from the structures of mRNAs and their relative abundance. These structures can be predicted using a bipartite information theory framework that is guided by current knowledge of biological mechanisms for exon recognition. We present the results of this analysis for the complete dataset of all expressed human exons. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CISS.2009.5054835 | Baltimore, MD |
Keywords | Field | DocType |
Monte Carlo methods,bioinformatics,information theory,macromolecules,organic compounds,Ab initio exon definition,Shannon information theory,biological mechanisms,information theory-based approach,mRNA,Biological System Modeling,Genetics,Information Theory,Monte Carlo Methods | Information theory,Mathematical optimization,Gene,Computer science,splice,Exon,Genomics,RNA splicing,Ab initio,Bioinformatics,Computational biology | Conference |
ISBN | Citations | PageRank |
978-1-4244-2734-5 | 1 | 0.35 |
References | Authors | |
4 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter K Rogan | 1 | 40 | 3.14 |