Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding schemeReportar como inadecuado




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BMC Bioinformatics

, 7:32

First Online: 22 January 2006Received: 03 June 2005Accepted: 22 January 2006

Abstract

BackgroundGene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene expression data, usually in the form of large matrix, may contain missing values. The downstream analysis methods that postulate complete matrix input are thus not applicable. Several methods have been developed to solve this problem, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. In this paper, we introduce a novel imputing approach based on the Support Vector Regression SVR method. The proposed approach utilizes an orthogonal coding input scheme, which makes use of multi-missing values in one row of a certain gene expression profile and imputes the missing value into a much higher dimensional space, to obtain better performance.

ResultsA comparative study of our method with the previously developed methods has been presented for the estimation of the missing values on six gene expression data sets. Among the three different input-vector coding schemes we tried, the orthogonal input coding scheme obtains the best estimation results with the minimum Normalized Root Mean Squared Error NRMSE. The results also demonstrate that the SVR method has powerful estimation ability on different kinds of data sets with relatively small NRMSE.

ConclusionThe SVR impute method shows better performance than, or at least comparable with, the previously developed methods in present research. The outstanding estimation ability of this impute method is partly due to the use of the most missing value information by incorporating orthogonal input coding scheme. In addition, the solid theoretical foundation of SVR method also helps in estimation of performance together with orthogonal input coding scheme. The promising estimation ability demonstrated in the results section suggests that the proposed approach provides a proper solution to the missing value estimation problem. The source code of the SVR method is available from http:-202.38.78.189-downloads-svrimpute.html for non-commercial use.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-7-32 contains supplementary material, which is available to authorized users.

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Autor: Xian Wang - Ao Li - Zhaohui Jiang - Huanqing Feng

Fuente: https://link.springer.com/



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