--- _id: '306' abstract: - lang: eng text: 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. article_number: e00596 author: - first_name: Andrea full_name: De Martino, Andrea last_name: De Martino - first_name: Daniele full_name: De Martino, Daniele id: 3FF5848A-F248-11E8-B48F-1D18A9856A87 last_name: De Martino orcid: 0000-0002-5214-4706 citation: ama: De Martino A, De Martino D. An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. 2018;4(4). doi:10.1016/j.heliyon.2018.e00596 apa: De Martino, A., & De Martino, D. (2018). An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. Elsevier. https://doi.org/10.1016/j.heliyon.2018.e00596 chicago: De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” Heliyon. Elsevier, 2018. https://doi.org/10.1016/j.heliyon.2018.e00596. ieee: A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” Heliyon, vol. 4, no. 4. Elsevier, 2018. ista: De Martino A, De Martino D. 2018. An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. 4(4), e00596. mla: De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” Heliyon, vol. 4, no. 4, e00596, Elsevier, 2018, doi:10.1016/j.heliyon.2018.e00596. short: A. De Martino, D. De Martino, Heliyon 4 (2018). date_created: 2018-12-11T11:45:44Z date_published: 2018-04-01T00:00:00Z date_updated: 2021-01-12T07:40:46Z day: '01' ddc: - '530' department: - _id: GaTk doi: 10.1016/j.heliyon.2018.e00596 ec_funded: 1 file: - access_level: open_access checksum: 67010cf5e3b3e0637c659371714a715a content_type: application/pdf creator: dernst date_created: 2019-02-06T07:36:24Z date_updated: 2020-07-14T12:45:59Z file_id: '5929' file_name: 2018_Heliyon_DeMartino.pdf file_size: 994490 relation: main_file file_date_updated: 2020-07-14T12:45:59Z has_accepted_license: '1' intvolume: ' 4' issue: '4' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '04' oa: 1 oa_version: Published Version project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Heliyon publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: 1 status: public title: An introduction to the maximum entropy approach and its application to inference problems in biology tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 4 year: '2018' ...