Information Theory in Computational Biology: Where We Stand Today
| Autor: | Eduardo Costa, Rasna Walia, John Van Van Hemert, Shravan Sukumar, Jie Hu, Pritam Chanda |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
error correction
Theoretical computer science General Physics and Astronomy Inference lcsh:Astrophysics Information theory Article 03 medical and health sciences transcriptomics 0302 clinical medicine computational biology interaction analysis lcsh:QB460-466 Entropy (information theory) protein structure lcsh:Science 030304 developmental biology information theory 0303 health sciences sequence comparison Cornerstone Probability and statistics disease-gene association mapping metabolomics lcsh:QC1-999 Mathematical theory gene expression metabolic networks lcsh:Q Error detection and correction entropy lcsh:Physics 030217 neurology & neurosurgery Data compression |
| Zdroj: | Entropy Volume 22 Issue 6 Entropy, Vol 22, Iss 627, p 627 (2020) |
| ISSN: | 1099-4300 |
| Popis: | &ldquo A Mathematical Theory of Communication&rdquo was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon&rsquo s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology&mdash gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis. |
| Databáze: | OpenAIRE |
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