A Stack-Based Ensemble Framework for Detecting Cancer MicroRNA Biomarkers

Abstract

MicroRNA (miRNA) plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between oncogenesis and expression profiles of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classification of miRNAs into different categories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classification problems related to cancer, miRNA, and mRNA expression datasets. In the first stage, a multiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classifier type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in the first stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression profile datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classification.

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification

“…This methodology can be applied in other problems, such as differentiating between tumors with and without metastasis ( Appendix C ), and it is not restricted to only miRNAs but can also be used in mRNA data. In contrast to other methods such as Saha et al [ 20 ], it is not limited by the number of variables ( Appendix D ).…”

“…While other techniques such as [ 20 ] can also be effective to identify signatures for bioinformatic applications, they are usually limited to working with a few hundreds of features. In order to show how our algorithm can be effective even with a large number of features, we apply it to dataset GSE12452 [ 102 ] that contains 54,675 features related to messenger RNA (mRNA).…”

Identification of miRNA Biomarkers for Diverse Cancer Types Using Statistical Learning Methods at the Whole-Genome Scale

“…The performance of the proposed method (SCES-FS) was tested on real miRNA expression datasets for 10 different cancer types and compared with the results of 16 existing methods, namely ESVM-RFE (Anaissi et al, 2016), LASSO (Tibshirani, 1996), NSGA-II-SE (Saha et al, 2017), MOGA (Mukhopadhyay and Maulik, 2013), SVM-nRFE (Peng et al, 2009), SVM-RFE (Guyon et al, 2002), CMIM (Fleuret, 2004), ICAP (Jakulin, 2005), SCAD (Fan and Li, 2001), JMI (Bennasar et al, 2015), CIFE (Brown et al, 2012), mRMR (Peng et al, 2005), FSCOX (Kim et al, 2014), DISR (Brown et al, 2012), SNRs (Mishra and Sahu, 2011), and RankSum (Troyanskaya…”

“…The experiment is conducted with data collected from The Cancer Genome Atlas (TCGA) 1 for 10 different types of cancer. The performance of the proposed wrapper-based feature selection method is compared with the following methods in terms of classification accuracy, with the top 17 miRNAs selected as putative biomarkers: ensemble SVM-recursive feature elimination (ESVM-RFE) (Anaissi et al, 2016), the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996), the non-dominated sorting genetic algorithm II-based stacked ensemble (NSGA-II-SE) (Saha et al, 2017), the SVM-wrapped multi-objective genetic algorithm (MOGA) (Mukhopadhyay and Maulik, 2013), SVM-based novel recursive feature elimination (SVM-nRFE) (Peng et al, 2009), SVM recursive feature elimination (SVM-RFE) (Guyon et al, 2002), conditional mutual information (CMIM) (Fleuret, 2004), interaction capping (ICAP) (Jakulin, 2005), smoothly clipped absolute deviation (SCAD) (Fan and Li, 2001), joint mutual information (JMI) (Bennasar et al, 2015), conditional infomax feature extraction (CIFE) (Brown et al, 2012), minimum redundancy maximum relevance (mRMR) (Peng et al, 2005), feature selection with Cox regression (FSCOX) (Kim et al, 2014), double-input symmetrical relevance (DISR) (Brown et al, 2012), signal-to-noise ratios (SNRs) (Mishra and Sahu, 2011), and the Wilcoxon ranksum test (RankSum) (Troyanskaya et al, 2002). Thereafter, the significance of the 17 selected miRNAs to 10 different cancer types is determined using Cox regression analysis.…”

Genome-Wide MicroRNA Expression Profiles in COPD: Early Predictors for Cancer Development

“…Different experimental approaches have been proposed for miRNA classification [35] . In the present study, we experienced a high performance in building prediction models using gradient boosted trees combined with selecting features (miRNAs) according to their ANOVA F -values.…”