Abstract
This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR)-based method for redefining the criterion function of f-information (FI) to identify the potential genes without discretizing the continuous gene expression values. The embedded procedure is implemented by means of a water swirl algorithm (WSA), which attempts to optimize the rule set and membership function required to classify samples using a fuzzy-rule-based multiclassification system (FRBMS). Two novel update equations are proposed in WSA, which have better exploration and exploitation abilities while designing a self-learning FRBMS. The efficiency of our new approach was evaluated on 13 multicategory and 9 binary datasets of cancer gene expression. Additionally, the performance of the proposed FRFI-WSA method in designing an FRBMS was compared with existing methods for gene selection and optimization such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony algorithm (ABC) on all the datasets. In the global cancer map with repeated measurements (GCM_RM) dataset, the FRFI-WSA showed the smallest number of 16 most relevant genes associated with cancer using a minimal number of 26 compact rules with the highest classification accuracy (96.45%). In addition, the statistical validation used in this study revealed that the biological relevance of the most relevant genes associated with cancer and their linguistics detected by the proposed FRFI-WSA approach are better than those in the other methods. The simple interpretable rules with most relevant genes and effectively classified samples suggest that the proposed FRFI-WSA approach is reliable for classification of an individual’s cancer gene expression data with high precision and therefore it could be helpful for clinicians as a clinical decision support system.
Highlights
Multiclass classification of gene expression data with a reduced number of genes remain challenging problems in cancer diagnosis
To implement an fuzzyrule-based multiclassification system (FRBMS) for a gene expression-based cancer diagnosis problem, identification of most relevant genes associated with cancer from the large set of genes is mandatory [4, 15]
We propose a new combined FRFI-water swirl algorithm (WSA) approach for designing an FRBMS to analyze gene expression data for cancer diagnosis
Summary
Multiclass classification of gene expression data with a reduced number of genes remain challenging problems in cancer diagnosis. Microarrays and next-generation sequencing [1, 2] are the chief tools of cancer research for quantification of gene expression, DNA copy number, and microRNA activity of each individual. Analyzing such data could give researchers useful information about the mechanism and cause of cancer and a way to predict and prevent cancer and to find possible novel treatments. The implementation of artificial intelligence using datamining tasks such as classification and clustering techniques has been applied to analyze gene expression values for cancer diagnosis [4,5,6,7]. These techniques suffered by a greater computational cost and training time
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