NDLI: DNA copy number selection using robust structured sparsity-inducing norms

Content Provider	ACM Digital Library
Author	Makedon, Fillia Metsis, Vangelis Shen, Dinggang Huang, Heng
Copyright Year	2014
Abstract	Array comparative genomic hybridization (aCGH) is a newly introduced method for the detection of copy number abnormalities associated with human diseases with special focus on cancer. Specific patterns in DNA copy number variations (CNVs) can be associated with certain disease types and can facilitate prognosis and progress monitoring of the disease. Machine learning techniques have been used to model the problem of tissue typing as a classification problem. Feature selection is an important part of the classification process, because many biological features are not related to the diseases and confuse the classification tasks. Multiple feature selection methods have been proposed in the different domains where classification has been applied. In this work, we will present a new feature selection method based on structured sparsityinducing norms to identify the informative aCGH biomarkers which can help us classify different disease subtypes. To validate the performance of the proposed method, we experimentally compare it with existing feature selection methods on four publicly available aCGH data sets. In all empirical results, the proposed sparse learning based feature selection method consistently outperforms other related approaches. More important, we carefully investigate the aCGH biomarkers selected by our method, and the biological evidences in literature strongly support our results.
Starting Page	138
Ending Page	181
Page Count	44
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2013.141
Volume Number	11
Issue Number	1
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-01-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	DNA copy number variations ACGH Biomarker detection Cancer classification Feature evaluation and selection
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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