Book Chapter10.1007/3-540-45333-4_53
DNA Sequences Classification Based on Wavelet Packet Analysis
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TL;DR: The classification of two types of DNA sequences is studied in this paper and 20 sample artificial DNA sequences whose types have been known are given to recognize the types of other DNA sequences.
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Abstract: The classification of two types of DNA sequences is studied in this paper. 20 sample artificial DNA sequences whose types have been known are given to recognize the types of other DNA sequences. Wavelet packet analysis is used to extract the features of the sample DNA sequences.
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Citations
On DNA numerical representations for genomic similarity computation.
Gerardo Mendizabal-Ruiz,Israel Román-Godínez,Sulema Torres-Ramos,Ricardo A. Salido-Ruiz,J. Alejandro Morales +4 more
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TL;DR: A hybrid PAS recognition model based on deep neural networks and logistic regression models is developed, which outperforms the well-tuned state-of-the-art Omni-PolyA models, reducing the classification error for different PAS hexamers by up to 57.35% for 10 out of 12 PAS types.
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Computational Micromodel for Epigenetic Mechanisms
TL;DR: In this article, the relation between DNA methylation and transcription was explored and the histone modifications for specific DNA methylations levels using a stochastic approach, and the results showed that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation.
Classification of non stationary signals using multiscale decomposition
TL;DR: An automatic algorithm for the classification of non stationary signals based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose is developed and more than 85% of events are well classified whatever the term of gestation.
Wavelet Neural Networks for DNA Sequence Classification Using the Genetic Algorithms and the Least Trimmed Square
TL;DR: The experimental results showed that the WNN-GA model outperformed the other models in terms of both the clustering results and the running time and the experimental results have indicated that the proposed method with the k-means algorithm is more precise than other methods.
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References
•Book
Wavelet Theory and Its Application to Pattern Recognition
Yuan Yan Tang
- 13 Mar 2000
TL;DR: This 2nd edition is an update of the book "Wavelet Theory and Its Application to Pattern Recognition" published in 2000 and provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition.
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