Next Article in Journal
Imprecise Shannon’s Entropy and Multi Attribute Decision Making
Previous Article in Journal
Estimation of Seismic Wavelets Based on the Multivariate Scale Mixture of Gaussians Model
Open AccessReview

Data Compression Concepts and Algorithms and Their Applications to Bioinformatics

Department of Electrical Engineering, University of Nebraska-Lincoln, NE 68588-0511, USA
*
Author to whom correspondence should be addressed.
Entropy 2010, 12(1), 34-52; https://doi.org/10.3390/e12010034
Received: 4 December 2009 / Accepted: 17 December 2009 / Published: 29 December 2009
Data compression at its base is concerned with how information is organized in data. Understanding this organization can lead to efficient ways of representing the information and hence data compression. In this paper we review the ways in which ideas and approaches fundamental to the theory and practice of data compression have been used in the area of bioinformatics. We look at how basic theoretical ideas from data compression, such as the notions of entropy, mutual information, and complexity have been used for analyzing biological sequences in order to discover hidden patterns, infer phylogenetic relationships between organisms and study viral populations. Finally, we look at how inferred grammars for biological sequences have been used to uncover structure in biological sequences. View Full-Text
Keywords: bioinformatics; data compression; information theory bioinformatics; data compression; information theory
Show Figures

Figure 1

MDPI and ACS Style

Nalbantoglu, Ö.U.; Russell, D.J.; Sayood, K. Data Compression Concepts and Algorithms and Their Applications to Bioinformatics. Entropy 2010, 12, 34-52.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop