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

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  • Richard R Stein
  • Debora S Marks
  • Chris Sander

Abstract

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.

Suggested Citation

  • Richard R Stein & Debora S Marks & Chris Sander, 2015. "Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-22, July.
  • Handle: RePEc:plo:pcbi00:1004182
    DOI: 10.1371/journal.pcbi.1004182
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    References listed on IDEAS

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    1. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
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    Cited by:

    1. Md Tauhidul Islam & Lei Xing, 2023. "Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Rajita Menon & Vivek Ramanan & Kirill S Korolev, 2018. "Interactions between species introduce spurious associations in microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-20, January.
    3. Andrew F Neuwald & Stephen F Altschul, 2016. "Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-30, December.
    4. Erik Aurell, 2016. "The Maximum Entropy Fallacy Redux?," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-7, May.

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