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Open AccessArticle

Information Theory in Computational Biology: Where We Stand Today

1
Corteva Agriscience™, Indianapolis, IN 46268, USA
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Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN 46202, USA
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Corteva Agriscience™, Mogi Mirim, Sao Paulo 13801-540, Brazil
4
Corteva Agriscience™, Johnston, IA 50131, USA
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(6), 627; https://doi.org/10.3390/e22060627
Received: 30 April 2020 / Revised: 31 May 2020 / Accepted: 3 June 2020 / Published: 6 June 2020
“A Mathematical Theory of Communication” 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’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—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. View Full-Text
Keywords: information theory; entropy; computational biology; gene expression; transcriptomics; sequence comparison; error correction; disease-gene association mapping; metabolic networks; metabolomics; protein structure; interaction analysis information theory; entropy; computational biology; gene expression; transcriptomics; sequence comparison; error correction; disease-gene association mapping; metabolic networks; metabolomics; protein structure; interaction analysis
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MDPI and ACS Style

Chanda, P.; Costa, E.; Hu, J.; Sukumar, S.; Van Hemert, J.; Walia, R. Information Theory in Computational Biology: Where We Stand Today. Entropy 2020, 22, 627. https://doi.org/10.3390/e22060627

AMA Style

Chanda P, Costa E, Hu J, Sukumar S, Van Hemert J, Walia R. Information Theory in Computational Biology: Where We Stand Today. Entropy. 2020; 22(6):627. https://doi.org/10.3390/e22060627

Chicago/Turabian Style

Chanda, Pritam; Costa, Eduardo; Hu, Jie; Sukumar, Shravan; Van Hemert, John; Walia, Rasna. 2020. "Information Theory in Computational Biology: Where We Stand Today" Entropy 22, no. 6: 627. https://doi.org/10.3390/e22060627

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