NDLI: A hypergraph-based learning algorithm for classifying arraycgh data with spatial prior

Content Provider	IEEE Xplore Digital Library
Author	Ze Tian TaeHyun Hwang Rui Kuang
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Engineering, University of Minnesota, Twin Cities, 200 Union Street SE, Minneapolis, 55455, USA (Ze Tian; TaeHyun Hwang; Rui Kuang)
Abstract	Array-based comparative genomic hybridization (array-CGH) has been used to detect DNA copy number variations at genome scale for molecular diagnosis and prognosis of cancer. A special property of arrayCGH data is that, among the spot-intensity variables in the arrayCGH data, there are spatial relations introduced by the layout of the probes along the chromosomes. Standard classification algorithms are not capable of capturing the spatial relations for accurate cancer classification or biomarker identification from the arrayCGH data. We introduce a hypergraph based learning algorithm to classify arrayCGH data with spatial priors modeled as correlations among variables for cancer classification and biomarker identification. In the experiments, we show that, by incorporating the spatial relations among the spots as prior, our algorithm is more accurate than other baseline algorithms on a bladder cancer array-CGH data. Furthermore, some discriminative regions identified by our algorithm contain genomic elements that are cancer-relavent.
Starting Page	1
Ending Page	4
File Size	2963502
Page Count	4
File Format	PDF
ISBN	9781424447619
DOI	10.1109/GENSIPS.2009.5174345
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines DNA Genomics Support vector machine classification Biomarkers Sampling methods Iterative algorithms Bioinformatics Biological cells Cancer
Content Type	Text
Resource Type	Article

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