Logo image
Discrete Wavelet Packet Transform Based Discriminant Analysis for Whole Genome Sequences
Journal article   Peer reviewed

Discrete Wavelet Packet Transform Based Discriminant Analysis for Whole Genome Sequences

Hsin-Hsiung Huang and Senthil Balaji Girimurugan
Statistical applications in genetics and molecular biology, Vol.18(2), 20180045
02-15-2019
PMID: 30772870

Abstract

asymptotic normal distribution classification discrete wavelet packet transform discriminant analysis viral genomes
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.

Metrics

12 Record Views
3 Times Cited - Scopus

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: SDGs in the Output

Logo image