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Feature selection is a useful pre-processing technique for solving classification problems. The challenge of solving the feature selection problem lies in applying evolutionary algorithms capable of handling the huge number of features typically involved. Generally, given classification data may contain useless, redundant or misleading features. To increase classification accuracy, the primary objective...
An operon is a fundamental unit of transcription contains a specific function of genes for the construction and regulation of networks at the whole genome level. The operon prediction is critical for the understanding of gene regulation and functions in newly sequenced genomes. Various methods for operon prediction have been proposed in the literature shows that the experimental methods for operon...
Background: High-throughput single nucleotide polymorphism (SNP) genotyping generates a huge amount of SNP data in genome-wide association studies. Simultaneous analyses for multiple SNP interactions associated with many diseases and cancers are essential; however, these analyses are still computationally challenging. Methods: In this study, we propose an odds ratio-based binary particle swarm optimization...
Many previous research papers have demonstrated that microarray gene expression data are useful for disease classification and medical diagnosis. Cancer microarray data normally have a particular characteristic where features (genes) greatly exceed the instance (tissue sample) numbers. Selecting appropriate numbers and relevant features to differentiate different types of cancer remains a challenge...
Feature selection is a useful pre-processing technique for solving classification problems. The challenge of using evolutionary algorithms lies in solving the feature selection problem caused by the number of features. Classification data may contain useless, redundant or misleading features. To increase the classification accuracy, the primary objective is to remove irrelevant features in the feature...
Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, they are limited by the cost of genotyping the tremendous number of SNPs. It is therefore essential to select only informative subsets (tag SNPs) out of all SNPs. Several promising methods for tag SNP selection have been proposed, such as the haplotype block-based and block-free approaches. The...
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