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

David Murrugarra
, Alan Veliz-Cuba

Producción científica: Comment/debate

1 Cita (Scopus)

Resumen

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.

Idioma original	English
Número de artículo	100617
Publicación	Patterns
Volumen	3
N.º	11
DOI	https://doi.org/10.1016/j.patter.2022.100617
Estado	Published - nov 11 2022

Nota bibliográfica

Publisher Copyright:
© 2022

Financiación

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.

Financiadores	Número del financiador
Simons Foundation	516088

ASJC Scopus subject areas

General Decision Sciences

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10.1016/j.patter.2022.100617

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

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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T2 - BoCSE

AU - Murrugarra, David

AU - Veliz-Cuba, Alan

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Y1 - 2022/11/11

N2 - 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.

AB - 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.

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UR - https://www.scopus.com/pages/publications/85141477041#tab=citedBy

U2 - 10.1016/j.patter.2022.100617

DO - 10.1016/j.patter.2022.100617

M3 - Comment/debate

AN - SCOPUS:85141477041

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JO - Patterns

JF - Patterns

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M1 - 100617

ER -

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

Resumen

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Financiación

ASJC Scopus subject areas

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