Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table

Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer

Methods for extracting marker genes that trigger the growth of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac

Genetic programming for mining DNA chip data from cancer patients

In machine learning terms DNA (gene) chip data is unusual in having thousands of attributes (the gene expression values) but few (<100) records (the patients). A GP based method for both feature selection and generating simple models based on a few genes is demonstrated on cancer data

A multiple-filter-multiple-wrapper approach to gene selection and microarray data classification

Filters and wrappers are two prevailing approaches for gene selection in microarray data analysis. Filters make use of statistical properties of each gene to represent its discriminating power between different classes. The computation is fast but the predictions are inaccurate. Wrappers make use of a chosen classifier to select genes by maximizing classification accuracy, but the computation burden is formidable. Filters and wrappers have been combined in previous studies to maximize the classification accuracy for a chosen classifier with respect to a filtered set of genes. The drawback of this single-filter-single-wrapper (SFSW) approach is that the classification accuracy is dependent on the choice of specific filter and wrapper. In this paper, a multiple-filter-multiple-wrapper (MFMW) approach is proposed that makes use of multiple filters and multiple wrappers to improve the accuracy and robustness of the classification, and to identify potential biomarker genes. Experiments based on six benchmark data sets show that the MFMW approach outperforms SFSW models (generated by all combinations of filters and wrappers used in the corresponding MFMW model) in all cases and for all six data sets. Some of MFMW-selected genes have been confirmed to be biomarkers or contribute to the development of particular cancers by other studies. © 2006 IEEE.published_or_final_versio

CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

For the last eight years, microarray-based class prediction has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the p > n setting where the number of predictors by far exceeds the number of observations, hence the term “ill-posed-problem”. Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for inexperienced users with limited statistical background or for statisticians without experience in this area. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers. In this article, we introduce a new Bioconductor package called CMA (standing for “Classification for MicroArrays”) for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches. CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at http://bioconductor.org/packages/2.3/bioc/html/CMA.html

CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

For the last eight years, microarray-based class prediction has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the p > n setting where the number of predictors by far exceeds the number of observations, hence the term “ill-posed-problem”. Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for inexperienced users with limited statistical background or for statisticians without experience in this area. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers. In this article, we introduce a new Bioconductor package called CMA (standing for “Classification for MicroArrays”) for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches. CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at http://bioconductor.org/packages/2.3/bioc/html/CMA.html

Gene selection and classification in autism gene expression data

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are currently diagnosed on the basis of abnormal stereotyped behaviour as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been attributed to the disorder, no single gene is applicable to more than 1–2% of the general ASD population. Despite extensive efforts, definitive genes that contribute to autism susceptibility have yet to be identified. The major problems in dealing with the gene expression dataset of autism include the presence of limited number of samples and large noises due to errors of experimental measurements and natural variation. In this study, a systematic combination of three important filters, namely t-test (TT), Wilcoxon Rank Sum (WRS) and Feature Correlation (COR) are applied along with efficient wrapper algorithm based on geometric binary particle swarm optimization-support vector machine (GBPSO-SVM), aiming at selecting and classifying the most attributed genes of autism. A new approach based on the criterion of median ratio, mean ratio and variance deviations is also applied to reduce the initial dataset prior to its involvement. Results showed that the most discriminative genes that were identified in the first and last selection steps concluded the presence of a repetitive gene (CAPS2), which was assigned as the most ASD risk gene. The fused result of genes subset that were selected by the GBPSO-SVM algorithm increased the classification accuracy to about 92.10%, which is higher than those reported in literature for the same autism dataset. Noticeably, the application of ensemble using random forest (RF) showed better performance compared to that of previous studies. However, the ensemble approach based on the employment of SVM as an integrator of the fused genes from the output branches of GBPSO-SVM outperformed the RF integrator. The overall improvement was ascribed to the selection strategies that were taken to reduce the dataset and the utilization of efficient wrapper based GBPSO-SVM algorithm