Discrete Wavelet Packet Transform Based Discriminant Analysis for Whole Genome Sequences
Huang Hsin-Hsiung () and
Girimurugan Senthil Balaji
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Huang Hsin-Hsiung: University of Central Florida, Department of Statistics, Orlando, FL, USA
Girimurugan Senthil Balaji: Florida Gulf Coast University, Department of Mathematics, Fort Myers, FL, USA
Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 2, 12
In recent years, alignment-free methods have been widely applied in comparing genome sequences, as these methods compute efficiently and provide desirable phylogenetic analysis results. These methods have been successfully combined with hierarchical clustering methods for finding phylogenetic trees. However, it may not be suitable to apply these alignment-free methods directly to existing statistical classification methods, because an appropriate statistical classification theory for integrating with the alignment-free representation methods is still lacking. In this article, we propose a discriminant analysis method which uses the discrete wavelet packet transform to classify whole genome sequences. The proposed alignment-free representation statistics of features follow a joint normal distribution asymptotically. The data analysis results indicate that the proposed method provides satisfactory classification results in real time.
Keywords: asymptotic normal distribution; classification; discrete wavelet packet transform; discriminant analysis; viral genomes (search for similar items in EconPapers)
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