Information Theory of Mixed Population Genome-Wide Association Studies

Tahmasebi, Behrooz; Maddah-Ali, Mohammad Ali; Motahari, Seyed Abolfazl

doi:10.1109/itw.2018.8613344

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…With increasing value of the model parameters N , G and L , the authors reported observing a threshold effect at

( H denoting binary entropy) that was then used to formulate the capacity of recovering the causal subsequence using information theory. This idea was further extended in a subsequent article [ 134 ] by comparing mixed and unmixed populations.…”

Section: Applications Of Information Theory In Computational Biolomentioning

confidence: 99%

Information Theory in Computational Biology: Where We Stand Today

Chanda

Costa²,

et al. 2020

Entropy

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“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.

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“…With increasing value of the model parameters N , G and L , the authors reported observing a threshold effect at

Section: Applications Of Information Theory In Computational Biolomentioning

confidence: 99%