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

Publisher Copyright:
© 2022

ASJC Scopus subject areas

General Decision Sciences

Access to Document

10.1016/j.patter.2022.100617

Cite this

@article{1e59e26c0da84fcca8da69d0828255f5,

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",

note = "Publisher Copyright: {\textcopyright} 2022",

year = "2022",

month = nov,

day = "11",

doi = "10.1016/j.patter.2022.100617",

language = "English",

volume = "3",

journal = "Patterns",

issn = "2666-3899",

publisher = "Cell Press",

number = "11",

}

TY - JOUR

T1 - An information theoretic approach for the inference of Boolean networks and functions from data

T2 - BoCSE

AU - Murrugarra, David

AU - Veliz-Cuba, Alan

PY - 2022/11/11

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.

UR - http://www.scopus.com/inward/record.url?scp=85141477041&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85141477041&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2022.100617

DO - 10.1016/j.patter.2022.100617

M3 - Comment/debate

AN - SCOPUS:85141477041

SN - 2666-3899

VL - 3

JO - Patterns

JF - Patterns

IS - 11

M1 - 100617

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this