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dc.creatorBarutcuoglu, Z
dc.creatorAiroldi, EM
dc.creatorDumeaux, V
dc.creatorSchapire, RE
dc.creatorTroyanskaya, OG
dc.date.accessioned2021-02-01T21:52:51Z
dc.date.available2021-02-01T21:52:51Z
dc.date.issued2009-05-01
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5580
dc.identifier.other19052061 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5598
dc.description.abstractMotivation: The heterogeneity of cancer cannot always be recognized by tumor morphology, but may be reflected by the underlying genetic aberrations. Array comparative genome hybridization (array-CGH) methods provide high-throughput data on genetic copy numbers, but determining the clinically relevant copy number changes remains a challenge. Conventional classification methods for linking recurrent alterations to clinical outcome ignore sequential correlations in selecting relevant features. Conversely, existing sequence classification methods can only model overall copy number instability, without regard to any particular position in the genome. Results: Here, we present the heterogeneous hidden conditional random field, a new integrated array-CGH analysis method for jointly classifying tumors, inferring copy numbers and identifying clinically relevant positions in recurrent alteration regions. By capturing the sequentiality as well as the locality of changes, our integrated model provides better noise reduction, and achieves more relevant gene retrieval and more accurate classification than existing methods. We provide an efficient L1-regularized discriminative training algorithm, which notably selects a small set of candidate genes most likely to be clinically relevant and driving the recurrent amplicons of importance. Our method thus provides unbiased starting points in deciding which genomic regions and which genes in particular to pursue for further examination. Our experiments on synthetic data and real genomic cancer prediction data show that our method is superior, both in prediction accuracy and relevant feature discovery, to existing methods. We also demonstrate that it can be used to generate novel biological hypotheses for breast cancer. © 2008 The Author(s).
dc.format.extent1307-1313
dc.language.isoen
dc.relation.haspartBioinformatics
dc.relation.isreferencedbyOxford University Press (OUP)
dc.rightsCC BY-NC
dc.subjectAlgorithms
dc.subjectAneuploidy
dc.subjectComparative Genomic Hybridization
dc.subjectComputational Biology
dc.subjectGene Dosage
dc.subjectNeoplasms
dc.subjectOligonucleotide Array Sequence Analysis
dc.titleAneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1093/bioinformatics/btn585
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidAiroldi, Edoardo|0000-0002-3512-0542
dc.date.updated2021-02-01T21:52:49Z
refterms.dateFOA2021-02-01T21:52:52Z


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