Stepwise classification of cancer samples using clinical and molecular data

BMC Bioinformatics

Research Article

Askar Obulkasim¹  Mark A van de Wiel²  Gerrit A Meijer³ 

[1] Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands;Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands;Department of Mathematics, VU University, Amsterdam, The Netherlands;Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands;

关键词: Partial Little Square;    Data Type;    Random Forest;    Molecular Data;    Classification Algorithm;   

DOI  :  10.1186/1471-2105-12-422

 received in 2011-05-27, accepted in 2011-10-28,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundCombining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient.ResultsWe introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples.ConclusionsOur method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals; moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.

【 授权许可】

CC BY   
© Obulkasim et al; licensee BioMed Central Ltd. 2011