Abstract
BackgroundFeature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-theoretic approaches. However, most of these methods generally require a long processing time. In addition, information-theoretic methods discretize continuous features, which is a drawback that can lead to the loss of information.ResultsIn this paper, a novel supervised feature scoring method named ClearF is proposed. The proposed method is suitable for continuous-valued data, which is similar to the principle of feature selection using mutual information, with the added advantage of a reduced computation time. The proposed score calculation is motivated by the association between the reconstruction error and the information-theoretic measurement. Our method is based on class-wise low-dimensional embedding and the resulting reconstruction error. Given multi-class datasets such as a case-control study dataset, low-dimensional embedding is first applied to each class to obtain a compressed representation of the class, and also for the entire dataset. Reconstruction is then performed to calculate the error of each feature and the final score for each feature is defined in terms of the reconstruction errors. The correlation between the information theoretic measurement and the proposed method is demonstrated using a simulation. For performance validation, we compared the classification performance of the proposed method with those of various algorithms on benchmark datasets.ConclusionsThe proposed method showed higher accuracy and lower execution time than the other established methods. Moreover, an experiment was conducted on the TCGA breast cancer dataset, and it was confirmed that the genes with the highest scores were highly associated with subtypes of breast cancer.
Highlights
Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics
An experiment was conducted on the The cancer genome atlas (TCGA) breast cancer dataset, and it was confirmed that the genes with the highest scores were highly associated with subtypes of breast cancer
The entropy of the multivariate Gaussian distribution can be calculated as follows, using the determinant of the covariance matrix [19]: HðXÞ 1⁄4 n þ n ln 2π þ ln j Σ j 22 where n is the number of features in X and Σ is the determinant of the covariance matrix
Summary
Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-theoretic approaches. Most of these methods generally require a long processing time. The ‘curse of dimensionality’ [7] occurs, in which the number of required samples exponentially increases as the number of features increases. To overcome this drawback, a feature selection method is often applied to the selection of important features. It is important to develop feature selection algorithms for the detection of biomarkers
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