Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data

Abstract

Over the last decade, high dimensional data have been popularly paid attention to in bioinformatics. These data increase the likelihood of detecting the most promising novel information. However, there are limitations of high-performance computing and overfitting issues. To overcome the issues, alternative strategies need to be explored for the detection of true important features. A two-stage approach, filtering and variable selection steps, has been receiving attention. Filtering methods are divided into two categories of individual ranking and feature subset selection methods. Both have issues with the lack of consideration for joint correlation among features and computing time of an NP-hard problem. Therefore, we proposed a new filter ranking method (PF) using the elastic net penalty with sure independence screening (SIS) based on resampling technique to overcome these issues. We demonstrated that SIS-LASSO, SIS-MCP, and SIS-SCAD with the proposed filtering method achieved superior performance of not only accuracy, AUROC, and geometric mean but also true positive detection compared to those with the marginal maximum likelihood ranking method (MMLR) through extensive simulation studies. In addition, we applied it in a real application of colon and lung cancer gene expression data to investigate the classification performance and power of detecting true genes associated with colon and lung cancer.

Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data

“…With this setup of high-dimensional data, we simulated three different types of data, each with correlation structures ρ = 0.2, 0.5, and 0.8 respectively. These values show the low, intermediate, and high correlation structures in the datasets which are significantly similar to what we usually see in the gene expression or others among many types of data in the field of bioinformatics [13,52]. At first, the data were divided randomly into training and testing sets with 75% and 25% of samples respectively; 75% of the training data was given to the FS methods, which ranked the genes concerning their importance, and then the top-ranked genes were selected based on b-SIS condition.…”

“…From [13], we see that the resampling-based FS is relatively more efficient in comparison to the other existing FS methods in gene expression data. The RLFS method is based on the lasso penalized regression method and the resampling approach employed to obtain the ranked important features using the frequency.…”

“…The FS methods are used to reduce the dimensionality of data by removing noisy and redundant features that help in selecting the truly important features. The FS methods are classified into rank-based and subset methods [12,13]. Rank-based methods rank all the features with respect to their importance based on some criteria.…”

“…In this article, we introduce the combination of an ensemble classifier with an FS method-the resampling-based lasso feature selection (RLFS) method for ranking features, and ensemble of regularized regression models (ERRM) for classification purposes. The resampling approach was proven to be one of the best FS screening steps in a high-dimensional data setting [13]. The RLFS uses the selection probability with lasso penalty, and the threshold for selecting the top-ranked features is set using b-SIS condition; and these select features were applied to the ERRM to achieve the best prediction accuracy.…”

Analyzing high dimensional correlated data using feature ranking and classifiers

“…Some of the existing studies for statistical comparison includes unsupervised clustering and normalization [20,21], supervised feature ranking and classi cation methods [22,23,19,24,25]. feature ranking and classi cation have been extensively utilized in microarray gene expression studies [26,27]. The key di erence of DNAm data from gene expression is that the DNAm has continuous variables ranging between 0 and 1.…”