Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.
Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4520494?pdf=render |
| Summary: | Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design. |
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| ISSN: | 1553-734X 1553-7358 |