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BIO-369: Randomness and information in biological data

Summary

Biology is becoming more and more a data science, as illustrated by the explosion of available genome sequences. This course aims to show how we can make sense of such data and harness it in order to understand biological processes in a quantitative way.

Lectures

Instructor

Anne-Florence Raphaëlle Bitbol

Official source

https://edu.epfl.ch/coursebook/en/randomness-and-information-in-biological-data-BIO-369

Moodle Page

http://go.epfl.ch/BIO-369

About this result

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Summary

Lectures

Instructor

Anne-Florence Raphaëlle Bitbol

Official source

https://edu.epfl.ch/coursebook/en/randomness-and-information-in-biological-data-BIO-369

Moodle Page

http://go.epfl.ch/BIO-369

About this result

Lectures in this course (28)

Quantifying Randomness in Biological Data

Covers randomness and information in biological data, focusing on discrete random variables and their quantification.

Discrete Random Variables: Medical Testing

Explores discrete random variables, joint probabilities, and medical testing quality using Bayes' theorem.

Quantifying Randomness and Information in Biological Data

Explores entropy, randomness, and information quantification in biological data analysis, including neuroscience and protein structure prediction.

Interpretation of Entropy

Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.

Quantifying Entropy in Neuroscience Data

Delves into quantifying entropy in neuroscience data, exploring how neuron activity represents sensory information and the implications of binary digit sequences.

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