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A unified computational model for revealing and predicting subtle subtypes of cancers
Ren,Xianwen1; Wang,Yong2,3; Wang,Jiguang4; Zhang,Xiang-Sun2,3
2012-05-01
Source PublicationBMC Bioinformatics
ISSN1471-2105
Volume13Issue:1
AbstractAbstractBackgroundGene expression profiling technologies have gradually become a community standard tool for clinical applications. For example, gene expression data has been analyzed to reveal novel disease subtypes (class discovery) and assign particular samples to well-defined classes (class prediction). In the past decade, many effective methods have been proposed for individual applications. However, there is still a pressing need for a unified framework that can reveal the complicated relationships between samples.ResultsWe propose a novel convex optimization model to perform class discovery and class prediction in a unified framework. An efficient algorithm is designed and software named OTCC (Optimization Tool for Clustering and Classification) is developed. Comparison in a simulated dataset shows that our method outperforms the existing methods. We then applied OTCC to acute leukemia and breast cancer datasets. The results demonstrate that our method not only can reveal the subtle structures underlying those cancer gene expression data but also can accurately predict the class labels of unknown cancer samples. Therefore, our method holds the promise to identify novel cancer subtypes and improve diagnosis.ConclusionsWe propose a unified computational framework for class discovery and class prediction to facilitate the discovery and prediction of subtle subtypes of cancers. Our method can be generally applied to multiple types of measurements, e.g., gene expression profiling, proteomic measuring, and recent next-generation sequencing, since it only requires the similarities among samples as input.
KeywordClass discovery Class prediction Quadratic programming Cancer
DOI10.1186/1471-2105-13-70
Language英语
WOS IDBMC:10.1186/1471-2105-13-70
PublisherBioMed Central
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/194
Collection应用数学研究所
Corresponding AuthorWang,Yong; Zhang,Xiang-Sun
Affiliation1.Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College; MOH Key Laboratory of Systems Biology of Pathogens
2.Chinese Academy of Sciences; Academy of Mathematics and Systems Science
3.Chinese Academy of Sciences; National Center for Mathematics and Interdisciplinary Sciences
4.Beijing Institute of Genomics, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Ren,Xianwen,Wang,Yong,Wang,Jiguang,et al. A unified computational model for revealing and predicting subtle subtypes of cancers[J]. BMC Bioinformatics,2012,13(1).
APA Ren,Xianwen,Wang,Yong,Wang,Jiguang,&Zhang,Xiang-Sun.(2012).A unified computational model for revealing and predicting subtle subtypes of cancers.BMC Bioinformatics,13(1).
MLA Ren,Xianwen,et al."A unified computational model for revealing and predicting subtle subtypes of cancers".BMC Bioinformatics 13.1(2012).
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