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Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1515-1
Pubmed ID
Authors

Yiming Zuo, Yi Cui, Guoqiang Yu, Ruijiang Li, Habtom W. Ressom

Abstract

Conventional 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. Through 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. The 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 105 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 26%
Researcher 16 15%
Student > Bachelor 15 14%
Student > Master 9 9%
Student > Doctoral Student 5 5%
Other 15 14%
Unknown 18 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 22 21%
Agricultural and Biological Sciences 17 16%
Computer Science 15 14%
Medicine and Dentistry 10 10%
Engineering 7 7%
Other 14 13%
Unknown 20 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 August 2017.
All research outputs
#9,317,921
of 11,653,629 outputs
Outputs from BMC Bioinformatics
#3,565
of 4,286 outputs
Outputs of similar age
#233,750
of 328,241 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 136 outputs
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