--- res: bibo_abstract: - A cornerstone of statistical inference, the maximum entropy framework is being increasingly applied to construct descriptive and predictive models of biological systems, especially complex biological networks, from large experimental data sets. Both its broad applicability and the success it obtained in different contexts hinge upon its conceptual simplicity and mathematical soundness. Here we try to concisely review the basic elements of the maximum entropy principle, starting from the notion of ‘entropy’, and describe its usefulness for the analysis of biological systems. As examples, we focus specifically on the problem of reconstructing gene interaction networks from expression data and on recent work attempting to expand our system-level understanding of bacterial metabolism. Finally, we highlight some extensions and potential limitations of the maximum entropy approach, and point to more recent developments that are likely to play a key role in the upcoming challenges of extracting structures and information from increasingly rich, high-throughput biological data.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Andrea foaf_name: De Martino, Andrea foaf_surname: De Martino - foaf_Person: foaf_givenName: Daniele foaf_name: De Martino, Daniele foaf_surname: De Martino foaf_workInfoHomepage: http://www.librecat.org/personId=3FF5848A-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-5214-4706 bibo_doi: 10.1016/j.heliyon.2018.e00596 bibo_issue: '4' bibo_volume: 4 dct_date: 2018^xs_gYear dct_language: eng dct_publisher: Elsevier@ dct_title: An introduction to the maximum entropy approach and its application to inference problems in biology@ ...