MaskedPainter: Feature selection for microarray data analysis

Apiletti, Daniele; Baralis, Elena; Bruno, Giulia; Fiori, Alessandro

doi:10.3233/ida-2012-0546

Cited by 14 publications

(44 citation statements)

References 51 publications

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“…Jirapech and Aitken [26] have analyzed several gene selection methods available in [9] and have shown that the gene selection method can have a significant impact on a classifier’s accuracy. Such a strategy has been applied in many studies including [7] and [8]. …”

Section: Resultsmentioning

confidence: 99%

“…To avoid bias, gene selection algorithms have been performed only on the training sets. For each fold, the best subset of genes has been selected according to the Wilcoxon Rank Sum technique (Wil‐RS), Minimum Redundancy Maximum Relevance (mRMR) method [8], MaskedPainter (MP) [7], Iteratively Sure Independent Screening (ISIS) [12], along‐with our proposed method. The expressions of the selected genes as well as the class labels of the training samples have then been used to construct the considered classifiers.…”

Section: Resultsmentioning

confidence: 99%

“…Since the total number of subspaces with dimension not higher than r is , where P is the total number of genes, it is hard to search the subspaces exhaustively [8]. Alternatively, various search schemes have been proposed e.g., best individual genes [9], Max‐Relevance and Min‐Redundancy based approaches [8], Iteratively Sure Independent Screening [12] and MaskedPainter approach [7]. Identification of discriminative genes can be based on different criteria including: p‐values of statistical tests e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, POS presents a novel generalized version, called POS score, of the overlapping score (OS) measure, proposed in [7]. …”

Section: Introductionmentioning

confidence: 99%

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A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

et al. 2014

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BackgroundMicroarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature’s relevance to a classification task.ResultsWe apply POS, along‐with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.ConclusionsA novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along‐with a novel gene score are exploited to produce the selected subset of genes.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-274) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In this context, POS presents a novel generalized version, called POS score, of the overlapping score (OS) measure, proposed in [7]. …”

Section: Introductionmentioning

confidence: 99%

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A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

et al. 2014

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“…Mining high‐throughput‐omics data and extracting biomarkers for accurate disease diagnosis is of great interest . However, after decades of effort, few biomarkers have been found and approved by the FDA .…”

Section: Introductionmentioning

confidence: 99%

A Robust and Efficient Feature Selection Algorithm for Microarray Data

Bari

Salekin

Zhang

2016

Molecular Informatics

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In the past decades, a few synergistic feature selection algorithms have been published, which includes Cooperative Index (CI) and K-Top Scoring Pair (k-TSP). These algorithms consider the synergistic behavior of features when they are included in a feature panel. Although promising results have been shown for these algorithms, there is lack of a comprehensive and fair comparison with other feature selection algorithms across a large number of microarray datasets in terms of classification accuracy and computational complexity. There is a need in evaluating their performance and reducing the complexity of such algorithms. We compared the performance of synergistic feature selection algorithms with 11 other commonly used algorithms based on 22 microarray gene expression binary class datasets. The evaluation confirms that synergistic algorithms such as CI and k-TSP will gradually increase the classification performance as more features are used in the classifiers. Also, in order to cut down computational cost, we proposed a new feature selection ranking score called Positive Synergy Index (PSI). Testing results show that features selected using PSI as well as synergistic feature selection algorithms provide better performance compared to with all other methods, while PSI has a computational complexity significantly lower than that of other synergistic algorithms.

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Minimizing Redundancy Among Genes Selected Based on the Overlapping Analysis

Mahmoud

Harrison

Gul

et al. 2016

Studies in Classification, Data Analysis, and Knowledge Organization

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract. For many functional genomic experiments, identifying the most characterizing genes is a main challenge. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on a set of discriminative genes. Analyzing overlapping between gene expression of different classes is an effective criterion for identifying relevant genes. However, genes selected according to maximizing a relevance score could have rich redundancy. We propose a scheme for minimizing selection redundancy, in which the Proportional Overlapping Score (POS) technique is extended by using a recursive approach to assign a set of complementary discriminative genes. The proposed scheme exploits the gene masks defined by POS to identify more integrated genes in terms of their classification patterns. The approach is validated by comparing its classification performance with other feature selection methods, Wilcoxon Rank Sum, mRMR, MaskedPainter and POS, for several benchmark gene expression datasets using three different classifiers: Random Forest; k Nearest Neighbour; Support Vector Machine. The experimental results of classification error rates show that our proposal achieves a better performance.

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MaskedPainter: Feature selection for microarray data analysis

Cited by 14 publications

References 51 publications

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

A feature selection method for classification within functional genomics experiments based on the proportional overlapping score

A Robust and Efficient Feature Selection Algorithm for Microarray Data

Minimizing Redundancy Among Genes Selected Based on the Overlapping Analysis

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