Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions

Research output: Contribution to journalConference Contribution

Abstract

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology,
an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions.

Fingerprint

Boolean Networks
Gene Expression Data
Gene expression
Technology
Gene Expression
Genetic Regulatory Networks
Formal Analysis
Bioinformatics
Microarrays
Computational Biology
Data Acquisition
Microarray
High Throughput
Predictors
Pathway
Data acquisition
Time-varying
Throughput
Series
Modeling

Keywords

  • gene expression profiles has
  • biomedicine
  • context sensitive
  • boolean networks

Cite this

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title = "Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions",
abstract = "In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology,an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions.",
keywords = "gene expression profiles has, biomedicine, context sensitive, boolean networks",
author = "Le Yu and Stephen Marshall",
year = "2007",
doi = "10.1186/1752-0509-1-S1-P63",
language = "English",
volume = "1",
pages = "63",
journal = "BMC Systems Biology",
issn = "1752-0509",
number = "Suppl 1",

}

Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions. / Yu, Le; Marshall, Stephen.

In: BMC Systems Biology, Vol. 1, No. Suppl 1, 2007, p. 63.

Research output: Contribution to journalConference Contribution

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T1 - Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions

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AU - Marshall, Stephen

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N2 - In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology,an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions.

AB - In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology,an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions.

KW - gene expression profiles has

KW - biomedicine

KW - context sensitive

KW - boolean networks

U2 - 10.1186/1752-0509-1-S1-P63

DO - 10.1186/1752-0509-1-S1-P63

M3 - Conference Contribution

VL - 1

SP - 63

JO - BMC Systems Biology

T2 - BMC Systems Biology

JF - BMC Systems Biology

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ER -