alexa Improved gene prediction by principal component analysis based autoregressive Yule-Walker method.
Agri and Aquaculture

Agri and Aquaculture

Journal of Marine Science: Research & Development

Author(s): Roy M, Barman S

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Abstract Spectral analysis using Fourier techniques is popular with gene prediction because of its simplicity. Model-based autoregressive (AR) spectral estimation gives better resolution even for small DNA segments but selection of appropriate model order is a critical issue. In this article a technique has been proposed where Yule-Walker autoregressive (YW-AR) process is combined with principal component analysis (PCA) for reduction in dimensionality. The spectral peaks of DNA signal are used to detect protein-coding regions based on the 1/3 frequency component. Here optimal model order selection is no more critical as noise is removed by PCA prior to power spectral density (PSD) estimation. Eigenvalue-ratio is used to find the threshold between signal and noise subspaces for data reduction. Superiority of proposed method over fast Fourier Transform (FFT) method and autoregressive method combined with wavelet packet transform (WPT) is established with the help of receiver operating characteristics (ROC) and discrimination measure (DM) respectively. Copyright © 2015 Elsevier B.V. All rights reserved. This article was published in Gene and referenced in Journal of Marine Science: Research & Development

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