Identifying differentially expressed genes from microarray experiments via statistic synthesis

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development.

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“…As a result, no more than four component feature selectors are used in the ensemble learning process [30,31,32,33].…”

Section: Function Perturbationmentioning

confidence: 99%

Stable feature selection for biomarker discovery

2010

Computational Biology and Chemistry

276

186

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“…Knowing that, there is often not a single universally optimal feature selection technique [23], it is useful to consider the hybridization of some FS methods, which can yield a more compact feature subset. This aspect is inspired by ensemble learning where, in a similar spirit, various classifiers are integrated to obtain a stronger classifier.…”

Section: Scope Of the Workmentioning

confidence: 99%

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Gupta

Bhavsar

2018

J. Imaging

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Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

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“…To avoid such drawbacks many hybrid approaches have been introduced like (Shahzad and Baig, 2010). Yang et al (2005) concluded that there is no any specific criteria which can be used to pick any filtered algorithm. They introduced a hybrid algorithm by merging different filtered based algorithms and produced better results.…”

Section: Related Workmentioning

confidence: 99%

Feature subset selection using association rule mining and JRip classifier

Shahzad¹,

Asad²,

Khan³

2013

Int. J. Phys. Sci.

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Feature selection is an important task in many fields such as statistics and machine learning. It aims at preprocessing step that include removal of irrelevant and redundant features and the retention of useful features. Selecting the relevant features increases the accuracy and decreases the computational cost. Feature selection also helps to understand the relevant data, addressing the complexity of dimensionality. In this paper, we have proposed a technique that uses JRip classifier and association rule mining to select the most relevant features from a data set. JRip extracts the rules from a data set and then association rules mining technique is applied to rank the features. Twenty datasets are tested ranging from binary class problem to multi-class problem. Extensive experimentation is carried out and the proposed technique is evaluated against the performance of various familiar classifiers. Experimental results demonstrate that while employing less number of features the proposed method achieves higher classification accuracy as well as generates less number of rules.

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Identifying differentially expressed genes from microarray experiments via statistic synthesis

Cited by 85 publications

References 28 publications

Stable feature selection for biomarker discovery

Stable feature selection for biomarker discovery

Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Feature subset selection using association rule mining and JRip classifier

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