The importance of network-based approach to identifying biological markers for diag-
nostic classification and prognostic assessment in the context of microarray has been
increasingly recognized. Standard methods treat all genes independently and identically
a priori and ignore the biological observation that genes function together in biological
processes. For binary classification, we are motivated to improve predictive accuracy
and gene selection by developing novel network-based classification tools that explicitly
incorporate interrelationships of genes as described by gene networks.
We propose three network-based support vector machines (SVM) by suitably forming
the penalty term. The neighboring-gene (NG) penalty groups pairwise gene neighbors
and sums up the L1-norm of each group over the entire network, leading to NG-SVM.
NG-SVM tends to select pairs of neighboring genes. The disease-gene-centric (DGC)
penalty is constructed on groups defined on an upper-lower hierarchy imposed on the
undirected network. DGC-SVM aims to detect collectives of genes clustering together
and around some key disease genes. The truncated L1-norm (TL1) penalty intends
to correct bias induced by penalization through a threshold parameter C > 0 built
into the L1-norm as used in NG-SVM and DGC-SVM. Simulation studies and real
data applications demonstrate that the proposed methods are able to capture more
disease genes and less noise genes than the existing popular methods, standard SVM
and L1-SVM. We conclude that the proposed methods have the potential to be effective
classification tools for microarrays and other high-dimensional data.
University of Minnesota Ph.D. dissertation. September 2009. Major: Biostatistics. Advisor: Wei Pan. 1 computer file (PDF); xii, 98 pages.
Network-based support vector machines for classification of microarray gene expression data..
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