Dataset Information

Bayesian network prior: network analysis of biological data using external knowledge.

ABSTRACT:

Motivation

Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event 'gene interaction' and is used to calculate the probability of a candidate graph (G) in the structure learning process.

Results

Our simulation results on synthetic, simulated and real biological data show that the proposed approach can identify the underlying interaction network with high accuracy even when the prior information is distorted and outperforms existing methods.

Availability

Accompanying BNP software package is freely available for academic use at http://bioe.bilgi.edu.tr/BNP.

Contact

hasan.otu@bilgi.edu.tr

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Isci S

PROVIDER: S-EPMC3957076 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Publications

Bayesian network prior: network analysis of biological data using external knowledge.

Isci Senol S Dogan Haluk H Ozturk Cengizhan C Otu Hasan H HH

Bioinformatics (Oxford, England) 20131109 6

<h4>Motivation</h4>Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we cal ...[more]

PMID: 24215027

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Project description:BackgroundConventional differential gene expression analysis by methods such as student's t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups.ResultsThrough simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method.ConclusionsThe proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.

Dataset Information

Bayesian network prior: network analysis of biological data using external knowledge.

Motivation

Results

Availability

Contact

Supplementary information

Publications

Bayesian network prior: network analysis of biological data using external knowledge.

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