Dimension reduction with redundant gene elimination for tumor classification
نویسندگان
چکیده
منابع مشابه
Dimension reduction for classification with gene expression microarray data.
An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) an...
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MOTIVATION One particular application of microarray data, is to uncover the molecular variation among cancers. One feature of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in the thousands. In statistical terms this very large number of predictors compared to a small number of samples or o...
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Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting al...
متن کاملIrrelevant gene elimination for Partial Least Squares based Dimension Reduction by using feature probes
It is hard to analyse gene expression data which has only a few observations but with thousands of measured genes. Partial Least Squares based Dimension Reduction (PLSDR) is superior for handling such high dimensional problems, but irrelevant features will introduce errors into the dimension reduction process. Here, feature selection is applied to filter the data and an algorithm named PLSDRg i...
متن کاملReducing Error of Tumor Classification by Using Dimension Reduction with Feature Selection
Dimension reduction is an important issue for analysis of gene expression microarray data, of which principle component analysis (PCA) is one of the frequently used methods, and in the previous works, the top several principle components are selected for modeling according to the descending order of eigenvalues. While in this paper, we argue that not all the first features are useful, but featu...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2008
ISSN: 1471-2105
DOI: 10.1186/1471-2105-9-s6-s8