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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

Richard R Stein¹, Debora S Marks², Chris Sander¹

¹Computational Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.

Plos Computational Biology

|July 31, 2015

Summary

This summary is machine-generated.

Maximum entropy models infer biological interactions from data. These methods, applicable to continuous and categorical variables, aid in protein structure prediction and gene network analysis.

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Area of Science:

Computational Biology
Bioinformatics
Statistical Modeling

Background:

Maximum entropy inference methods are vital for uncovering direct interactions in biological data like gene expression and sequence ensembles.
Undirected pairwise maximum-entropy probability models are categorized based on continuous and categorical random variables.

Purpose of the Study:

To review undirected pairwise maximum-entropy probability models for continuous and categorical data.
To present inference methods for protein contact prediction as a concrete example.
To demonstrate how similar solution strategies apply to both data types.

Main Methods:

Review of maximum entropy-based inference methods for biological data.
Application of these methods to protein contact prediction.
Analysis of model parameters reflecting interactive couplings.

Main Results:

Inference methods for continuous and categorical variables share similar solution strategies.
Model parameters quantify interactive couplings between observables.
These parameters can predict global biological system properties.

Conclusions:

Maximum entropy models provide a unified framework for inferring biological interactions across different data types.
The methods are applicable to protein 3-D structure prediction and gene-gene network association.
Potential applications include analysis of gene alteration patterns and protein design.