Information Theoretic Properties of Population Genetic Data

Omri Tal

Max Planck Institute for Mathematics in the Sciences, Leipzig

There has recently been renewed interest at borrowing both concepts and technical results from information theory for analysis in the biosciences. I will briefly review some efforts at incorporating notions such as entropy, channel capacity, communication noise, Kolmogorov complexity and mutual information into a biological framework, beyond merely as metaphorical. I will then describe a recent proposal that highlights intrinsic information-theoretic properties of genetic population samples. In essence, long stretches of genetic variants may be captured as messages generated by a nonstationary communication source modelled on a number of target populations. This perspective motivates the construction of simple population assignment schemes based on information theoretic quantities, where noise inherent to the learning process plays a similar role as channel noise in communication.


London Mathematical Laboratory, 14 Buckingham Street, London, WC2N 6DF

Date & Time

Monday 11th September 2017 at 14.00hrs


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