An introduction to the maximum entropy approach and its application to inference problems in biology Journal Article


Author(s): De Martino, Andrea; De Martino, Daniele
Article Title: An introduction to the maximum entropy approach and its application to inference problems in biology
Affiliation IST Austria
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.
Keywords: bioinformatics; Computational Biology; Molecular biology; systems biology; Mathematical bioscience
Journal Title: Heliyon
Volume: 4
Issue 4
ISSN: 24058440
Publisher: Elsevier  
Date Published: 2018-04-01
Start Page: Article number: e00596
DOI: 10.1016/j.heliyon.2018.e00596
Notes: This work was supported by the European Union’s Horizon 2020 Research and Innovation Staff Exchange program MSCA-RISE-2016 under grant agreement no. 734439 INFERNET, and by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under Research Executive Agency (REA) Grant Agreement No. 291734.
Open access: yes (OA journal)