Chanda, PritamCosta, EduardoHu, JieSukumar, ShravanVan Hemert, JohnWalia, Rasna2022-01-142022-01-142020-06Chanda, P., Costa, E., Hu, J., Sukumar, S., Van Hemert, J., & Walia, R. (2020). Information Theory in Computational Biology: Where We Stand Today. Entropy, 22(6), 627. https://doi.org/10.3390/e220606271099-4300https://hdl.handle.net/1805/27455"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.en-USAttribution 4.0 United Statescomputational biologydisease-gene association mappingentropyerror correctionmetabolic networksinteraction analysisArticle