A unified computational model for revealing and predicting subtle subtypes of cancers

BMC Bioinformatics

Research Article

Yong Wang¹  Xiang-Sun Zhang¹  Jiguang Wang²  Xianwen Ren³ 

[1] Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China;National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, 100190, Beijing, China;Beijing Institute of Genomics, Chinese Academy of Sciences, 7 Beitucheng West Road, 100029, Beijing, China;MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100730, Beijing, China;

关键词: Class discovery;    Class prediction;    Quadratic programming;    Cancer;   

DOI  :  10.1186/1471-2105-13-70

 received in 2011-12-10, accepted in 2012-04-04,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundGene 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.

【 授权许可】

Unknown   
© Ren et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.