A boosting approach to structure learning of graphs with and without prior knowledge

Anjum, Shahzia; Doucet, Arnaud; Holmes, Connor

doi:10.1093/bioinformatics/btp485

Cited by 17 publications

(12 citation statements)

References 31 publications

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“…These technological advances have made possible to investigate disease mechanisms, identify biomarkers [63], group genes into functional pathways [64], assign function to previously unannotated genes, and evaluate the toxicity of candidate drugs [65]. Among those technologies, microarrays have been widely utilized in clinical studies for their cost-effectiveness, their rapid and mature turnaround, and their ability to provide high sensitivity and specificity results from limited biological materials (nanograms).…”

Section: Discussionmentioning

confidence: 99%

A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray

et al. 2017

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BackgroundHuman endogenous retroviruses (HERVs) have received much attention for their implications in the etiology of many human diseases and their profound effect on evolution. Notably, recent studies have highlighted associations between HERVs expression and cancers (Yu et al., Int J Mol Med 32, 2013), autoimmunity (Balada et al., Int Rev Immunol 29:351–370, 2010) and neurological (Christensen, J Neuroimmune Pharmacol 5:326–335, 2010) conditions. Their repetitive nature makes their study particularly challenging, where expression studies have largely focused on individual loci (De Parseval et al., J Virol 77:10414–10422, 2003) or general trends within families (Forsman et al., J Virol Methods 129:16–30, 2005; Seifarth et al., J Virol 79:341–352, 2005; Pichon et al., Nucleic Acids Res 34:e46, 2006).MethodsTo refine our understanding of HERVs activity, we introduce here a new microarray, HERV-V3. This work was made possible by the careful detection and annotation of genomic HERV/MaLR sequences as well as the development of a new hybridization model, allowing the optimization of probe performances and the control of cross-reactions.ResultsHERV-V3 offers an almost complete coverage of HERVs and their ancestors (mammalian apparent LTR-retrotransposons, MaLRs) at the locus level along with four other repertoires (active LINE-1 elements, lncRNA, a selection of 1559 human genes and common infectious viruses). We demonstrate that HERV-V3 analytical performances are comparable with commercial Affymetrix arrays, and that for a selection of tissue/pathological specific loci, the patterns of expression measured on HERV-V3 is consistent with those reported in the literature.ConclusionsGiven its large HERVs/MaLRs coverage and additional repertoires, HERV-V3 opens the door to multiple applications such as enhancers and alternative promoters identification, biomarkers identification as well as the characterization of genes and HERVs/MaLRs modulation caused by viral infection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3669-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray

et al. 2017

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“…Boosting assembles a strong learner using a set of weak learners (Anjum et al ., 2009; Hastie et al ., 2009; Ma et al ., 2012; Schapire & Freund, 2012). It is appropriate for cancer genomic data as individual genes usually have weak effects, but combined together, they may have strong effects.…”

Section: Methodsmentioning

confidence: 99%

Gene network-based cancer prognosis analysis with sparse boosting

Huang

et al. 2012

Genet. Res.

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Summary High-throughput gene profiling studies have been extensively conducted, searching for markers associated with cancer development and progression. In this study, we analyse cancer prognosis studies with right censored survival responses. With gene expression data, we adopt the weighted gene co-expression network analysis (WGCNA) to describe the interplay among genes. In network analysis, nodes represent genes. There are subsets of nodes, called modules, which are tightly connected to each other. Genes within the same modules tend to have co-regulated biological functions. For cancer prognosis data with gene expression measurements, our goal is to identify cancer markers, while properly accounting for the network module structure. A two-step sparse boosting approach, called Network Sparse Boosting (NSBoost), is proposed for marker selection. In the first step, for each module separately, we use a sparse boosting approach for within-module marker selection and construct module-level ‘super markers ’. In the second step, we use the super markers to represent the effects of all genes within the same modules and conduct module-level selection using a sparse boosting approach. Simulation study shows that NSBoost can more accurately identify cancer-associated genes and modules than alternatives. In the analysis of breast cancer and lymphoma prognosis studies, NSBoost identifies genes with important biological implications. It outperforms alternatives including the boosting and penalization approaches by identifying a smaller number of genes/modules and/or having better prediction performance.

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“…The paper proposes a flexible Boosting regression (also known as Incremental Forward Stage-wise Regression [7]) algorithm as described below. The presented algorithm, to infer large GRNs from the microarray gene expression matrix X, comprises of the following steps.…”

Section: The Ipgrn Algorithmmentioning

confidence: 99%

Inferring gene regulatory networks with a scale-free property based informative prior

Yang

Liu

et al. 2015

2015 8th International Conference on Biomedical Engineering and Informatics (BMEI)

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constructing gene regulatory networks (GRNs) with microarray gene data is an essential and challenging task, especially when the underlying structures of networks are not observable in an experimental context. The paper proposes a boosting regression algorithm, called informative prior based GRN construction (ipGRN), to perform GRN inference. The ipGRN utilizes a scale-free based informative prior as well as Bayesian criterion measure to improve inference accuracy. In comparison with three existing methods (NIMOO, lasso and NIR), the ipGRN exhibits a significant improvement of computational accuracy and effectiveness on experiments of synthetic and real datasets. Furthermore, the method was applied to breast cancer data to reconstruct a sub-network of cancer susceptibility genes and achieved better inference results in detecting cancer associated genes.

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A boosting approach to structure learning of graphs with and without prior knowledge

Abstract: This method and all other associated files are freely available from http://www.stats.ox.ac.uk/~anjum/.

Cited by 17 publications

References 31 publications

A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray

A comprehensive hybridization model allows whole HERV transcriptome profiling using high density microarray

Gene network-based cancer prognosis analysis with sparse boosting

Inferring gene regulatory networks with a scale-free property based informative prior

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