---
_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'
...