Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

Jirapech-Umpai, Thanyaluk; Aitken, J. Stuart

doi:10.1186/1471-2105-6-148

Cited by 258 publications

(30 citation statements)

References 16 publications

(32 reference statements)

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“…SFS can also add constraints [32] on the size of the subset to be selected. It can be used in combination with a recursive support vector machine (R-SVM) algorithm that selects important genes or biomarkers [33]. The contribution factor , based on minimal error of the support vector machine, of each gene is calculated and ranked.…”

Section: Feature Subset Selection In Microarray Cancer Datamentioning

confidence: 99%

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Hira

Gillies

2015

Advances in Bioinformatics

773

472

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We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.

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Section: Feature Subset Selection In Microarray Cancer Datamentioning

confidence: 99%

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Hira

Gillies

2015

Advances in Bioinformatics

773

472

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“…Such an assessment can verify the efficiency of identification of discriminative genes. 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%

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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“…We carried out a feature selection using a range of methods in order to conduct a robust study. As in the case of the studies [45, 46], several methods were chosen and a comparison made with dataset containing the original features, improving the classification in nearly all cases when applying such methods. When creating the feature selection committee and disregarding those features that did not meet the threshold, improving accuracy in the case of the three classifiers.…”

Section: Discussionmentioning

confidence: 99%

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data

Garcia-Chimeno

Garcia‐Zapirain

Gómez-Beldarrain

et al. 2017

BMC Med Inform Decis Mak

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BackgroundFeature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition – factors that influence of pain perceptions.MethodsWe select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms.ResultsWhen classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions).ConclusionsThe proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging.

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Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

Cited by 258 publications

References 16 publications

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

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

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data

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