Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR)

Abstract: Bayesian network and linear regression methods have been widely applied to reconstruct cellular regulatory networks. In this work, we propose a Bayesian model averaging for linear regression (BMALR) method to infer molecular interactions in biological systems. This method uses a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene within a hybrid framework of Bayesian model averaging and linear regression methods. We have assessed the performance of BMALR by benchmarking on both in silico DREAM datasets and real experimental datasets. The results show that BMALR achieves both high prediction accuracy and high computational efficiency across different benchmarks. A pre-processing of the datasets with the log transformation can further improve the performance of BMALR, leading to a new top overall performance. In addition, BMALR can achieve robust high performance in community predictions when it is combined with other competing methods. The proposed method BMALR is competitive compared to the existing network inference methods. Therefore, BMALR will be useful to infer regulatory interactions in biological networks. A free open source software tool for the BMALR algorithm is available at https://sites.google.com/site/bmalr4netinfer/

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
Molecular BioSystems. 10, issue 8 (2014), 2023-2030 , DOI 10.1039/C4MB00053F, issn: 1742-2051
IN COPYRIGHT http://rightsstatements.org/page/InC/1.0 rs

Klassifikation
Mathematik

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2014
Urheber
Huang, Xun
Zi, Zhike
Beteiligte Personen und Organisationen
Fakultät für Biologie
BIOSS Centre for Biological Signalling Studies
Albert-Ludwigs-Universität Freiburg

DOI
10.1039/C4MB00053F
URN
urn:nbn:de:bsz:25-freidok-122987
Rechteinformation
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Letzte Aktualisierung
14.08.2025, 10:52 MESZ

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Beteiligte

  • Huang, Xun
  • Zi, Zhike
  • Fakultät für Biologie
  • BIOSS Centre for Biological Signalling Studies
  • Albert-Ludwigs-Universität Freiburg
  • Universität

Entstanden

  • 2014

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