Abstract
With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes.
Highlights
The advent of microarray technology has made it possible the study of the expression profiles of a huge number of genes across different experimental conditions or tissue samples simultaneously
When microarray datasets are organized as samples versus gene fashion, they are very helpful for classification of different types of tissues and identification of those genes whose expression levels are good diagnostic indicators
If the samples are from different subtypes of cancer, it becomes the problem of multi-class cancer classification
Summary
The advent of microarray technology has made it possible the study of the expression profiles of a huge number of genes across different experimental conditions or tissue samples simultaneously. The clustering solution produced by the proposed MOGASVM clustering technique has been used to identify the gene markers that are mostly responsible for distinguishing a particular tumor class from the remaining ones.
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