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Comparative and Functional Genomics (1)
Wang, Yue (3)
Xuan, Jianhua (3)
Ha, Sook (2)
Clarke, Robert (1)
Ha, Sook S. (1)
Hoffman, Eric (1)
Hoffman, Eric P. (1)
Kim, Inyoung (1)
Li, Huai (1)
Madhavan, Subha (1)
Shih, Ie−Ming (1)
Wang, Chen (1)
Yu, Guoqiang (1)
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Computational Analysis of Muscular Dystrophy Sub-types Using A Novel Integrative Scheme
To construct biologically interpretable gene sets for muscular dystrophy (MD) sub-type classification, we propose a novel computational scheme to integrate protein-protein interaction (PPI) network, functional gene set information, and mRNA profiling data. The workflow of the proposed scheme includes the following three major steps: firstly, we apply an affinity propagation clustering (APC) approach to identify gene sub-networks associated with each MD sub-type, in which a new distance metric is proposed for APC to combine PPI network information and gene-gene co-expression relationship; secondly, we further incorporate functional gene set knowledge, which complements the physical PPI information, into our scheme for biomarker identification; finally, based on the constructed sub-networks and gene set features, we apply multi-class support vector machines (MSVMs) for MD sub-type classification, with which to highlight the biomarkers contributing to sub-type prediction. The experimental results show that our scheme can help identify sub-networks and gene sets that are more relevant to MD than those constructed by other conventional approaches. Moreover, our integrative strategy improves the prediction accuracy substantially, especially for those ’hard-to-classify’ sub-types.
Gene expression; Classification; Muscular dystrophy; Affinity propagation clustering; Biomarker discovery
PUGSVM: a caBIGTM analytical tool for multiclass gene selection and predictive classification
Hoffman, Eric P.
Summary: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases.
Supplementary information: Supplementary data are available at Bioinformatics online.
Applications of Different Weighting Schemes to Improve Pathway-Based Analysis
Comparative and Functional Genomics
Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of P value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.
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