Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks

Kazmi, Saira A.; Kim, Yoo-Ah; Shin, Dong-Guk

doi:10.1504/ijdmb.2010.035896

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…we refer to the columns of U, the left singular vectors (Kazmi, S.A. et al, 2010) as eigenassays and the rows of V T , the right singular vectors {v k } as eigengenes. we refer to the columns of U, the left singular vectors (Kazmi, S.A. et al, 2010) as eigenassays and the rows of V T , the right singular vectors {v k } as eigengenes.…”

Section: Supervised Eigenvector Selectionmentioning

confidence: 99%

“…Filter-based approach selects genes according to the intrinsic characteristics without the incorporation of learning algorithms. As a remedy, some researchers validate the biological relevance of the found genes by searching over the biological knowledge bases such as Gene Ontology (GO) or uses extra knowledge in published gene regulation relationships to examine fi ndings in a microarray experiment (Kazmi et al, 2010). The wrapper approach or the supervised classifi cation method selects genes by proposing a classifi er with highest quality of classifi cation for some clinical or biological outcomes, such as Support Vector Machine Recursive Feature Elimination (SVM-RFE) (Guyon et al, 2002;Lancashire et al, 2008) and stepwise Artifi cial Neural Network (ANN) (Guyon et al, 2002;Lancashire et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach

Han

Lai

Xie

et al. 2014

IJDMB

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Identifying glioma cancer-alerted genetic markers through analysis of microarray data allows us to detect tumours at the genome-wide level. To this end, we propose to identify glioma gene markers based primarily on their correlation with the glioma diagnostic outcomes, rather than merely on the classification quality or differential expression levels, as it is not the classification or expression level per se that is crucial, but the selection of biologically relevant biomarkers is the most important issue. With the help of singular value decomposition, microarray data are decomposed and the eigenvectors corresponding to the biological effect of diagnostic outcomes are identified. Genes that play important roles in determining this biological effect are thus detected. Therefore, genes are essentially identified in terms of their strength of association with diagnostic outcomes. Monte Carlo simulations are then used to fine tune the selected gene set in terms of classification accuracy. Experiments show that the proposed method achieves better classification accuracies and is data sets independent. Graph-based statistical analysis showed that the selected genes have close relationships with glioma diagnostic outcomes. Further biological database and literature study confirms that the identified genes are biologically relevant.

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Section: Supervised Eigenvector Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach

Han

Lai

Xie

et al. 2014

IJDMB

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MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

et al. 2016

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Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

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Sample-space-based feature extraction and class preserving projection for gene expression data

Wang

2013

IJDMB

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In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

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Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks

Cited by 3 publications

References 27 publications

Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach

Identification of glioma cancer-alerted gene markers based on a diagnostic outcome correlation analysis preferential approach

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

Sample-space-based feature extraction and class preserving projection for gene expression data

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