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Advances in experimental medicine and biology
2021;1346 119-130.

Probabilistic Graphical Models Applied to Biological Networks.

Natalia Faraj Murad, Marcelo Mendes Brandão
Author Information
  1. Natalia Faraj Murad: Center for Molecular Biology and Genetic Engineering, State University of Campinas, Campinas, São Paulo, Brazil.
  2. Marcelo Mendes Brandão: Center for Molecular Biology and Genetic Engineering, State University of Campinas, Campinas, São Paulo, Brazil. brandaom@unicamp.br.
PMID: 35113399 DOI: 10.1007/978-3-030-80352-0_7

Abstract

Biological networks can be defined as a set of molecules and all the interactions among them. Their study can be useful to predict gene function, phenotypes, and regulate molecular patterns. Probabilistic graphical models (PGMs) are being widely used to integrate different data sources with modeled biological networks. The inference of these models applied to large-scale experiments of molecular biology allows us to predict influences of the experimental treatments in the behavior/phenotype of organisms. Here, we introduce the main types of PGMs and their applications in a biological networks context.

Keywords

Bioinformatics Biological networks System biology

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MeSH Term

Algorithms
Computational Biology
Gene Regulatory Networks
Information Storage and Retrieval
Models, Biological
Models, Statistical
Journal Article

Word Cloud

Created with Highcharts 10.0.0networksBiologicalcanpredictmolecularProbabilisticmodelsPGMsbiologicalbiologydefinedsetmoleculesinteractionsamongstudyusefulgenefunctionphenotypesregulatepatternsgraphicalwidelyusedintegratedifferentdatasourcesmodeledinferenceappliedlarge-scaleexperimentsallowsusinfluencesexperimentaltreatmentsbehavior/phenotypeorganismsintroducemaintypesapplicationscontextGraphicalModelsAppliedNetworksBioinformaticsSystem

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