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Course

BIO-369: Randomness and information in biological data

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.

Entropy in Neuroscience and Ecology

Delves into entropy in neuroscience data and ecology, exploring the representation of sensory information and the diversity of biological populations.

Quantifying Statistical Dependence: Covariance and Correlation

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Mutual Information: Understanding Random Variables

Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.

Mutual Information: Continued

Explores mutual information for quantifying statistical dependence between variables and inferring probability distributions from data.

Mutual Information in Biological Data

Explores mutual information in biological data, emphasizing its role in quantifying statistical dependence and analyzing protein sequences.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Luria-Delbrück Experiment: Testing Evolution Hypotheses

Explores the Luria-Delbrück experiment, demonstrating how probabilities are used to test evolution hypotheses.

Quantifying Statistical Dependence

Delves into quantifying statistical dependence through covariance, correlation, and mutual information.

Introduction to Maximum Entropy Inference

Introduces maximum entropy inference for finding optimal probability distributions with minimal extra structure.

Maximum Entropy Modeling: Applications & Inference

Explores maximum entropy modeling applications in neuroscience and protein sequence data.

Protein Structure Prediction

Explores predicting protein structure from sequence data using maximum entropy modeling and discusses recent advancements in protein structure prediction.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Nonlinear Dimension Reduction Methods

Explores nonlinear dimension reduction methods preserving neighborhoods in lower-dimensional spaces.

Biological Randomness and Data Analysis

Explores randomness in biology, covering thermal fluctuations, random walks, and data analysis techniques.