An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE

David Murrugarra; Alan Veliz-Cuba

doi:10.1016/j.patter.2022.100617

An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE

Research output: Contribution to journal › Comment/debate

Abstract

Building predictive models from data is an important and challenging task in many fields including biology, medicine, engineering, and economy. In this issue, Sun et al.¹ present a method for the inference of Boolean networks along with practical applications.

Original language	English
Article number	100617
Journal	Patterns
Volume	3
Issue number	11
DOIs	https://doi.org/10.1016/j.patter.2022.100617
State	Published - Nov 11 2022

Bibliographical note

Funding Information:
D.M. was partially supported by a Collaboration grant (850896) from the Simons Foundation . A.V.-C. was partially supported by the Simons Foundation grant 516088.

Publisher Copyright:
© 2022

ASJC Scopus subject areas

Decision Sciences (all)

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10.1016/j.patter.2022.100617

Cite this

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abstract = "Building predictive models from data is an important and challenging task in many fields including biology, medicine, engineering, and economy. In this issue, Sun et al.1 present a method for the inference of Boolean networks along with practical applications.",

author = "David Murrugarra and Alan Veliz-Cuba",

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An information theoretic approach for the inference of Boolean networks and functions from data: BoCSE

Abstract

Bibliographical note

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