Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.

/pdf/on-dna-numerical-representations-for-genomic-similarity-d9txzoy5tj.pdf

On DNA numerical representations for genomic similarity computation.

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Characterization of the epigenetic profile of humans since the initial breakthrough on the human genome project has strongly established the key role of histone modifications and DNA methylation. These dynamic elements interact to determine the normal level of expression or methylation status of the constituent genes in the genome. Recently, considerable evidence has been put forward to demonstrate that environmental stress implicitly alters epigenetic patterns causing imbalance that can lead to cancer initiation. This chain of consequences has motivated attempts to computationally model the influence of histone modification and DNA methylation in gene expression and investigate their intrinsic interdependency. In this paper, we explore the relation between DNA methylation and transcription and characterize in detail the histone modifications for specific DNA methylation levels using a stochastic approach.

/pdf/computational-micromodel-for-epigenetic-mechanisms-505rfotd84.pdf

Computational Micromodel for Epigenetic Mechanisms

The aim of this article is to develop an automatic algorithm for the classification of non stationary signals. The application context is to classify uterine electromyogram (EMG) events to prevent the onset of preterm birth. The idea is to discriminate between the events by allocating them to the physiological classes: contractions, foetus motions, Alvarez or Long Duration Low Frequency waves. Our method is based on the Wavelet Packet (WP) decomposition and the choice of a best basis for classification purpose. Before classification, there is a need to detect events in the recorded signals. The discrimination criterion is based on the calculation of the ratio between intra-class variance and total variance (sum of the intra-class and inter-class variances), calculated directly from the coefficients of the selected WP. We evaluated the performance of the algorithm on real signals by using the classification methods Neural Networks (NN) and Support Vector Machines (SVM). Subband energies of the best selected WP are used as effective features. The determined best basis is applicable to a wide range of uterine EMG signals from large range of patients. In most cases, more than 85% of events are well classified whatever the term of gestation.

/pdf/classification-of-non-stationary-signals-using-multiscale-27w8a3ijyc.pdf

Classification of non stationary signals using multiscale decomposition

Wavelet Neural Networks for DNA Sequence Classification Using the Genetic Algorithms and the Least Trimmed Square

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.

DNA Sequences Classification Based on Wavelet Packet Analysis

An accurately evaluate about the zone number and position of the gas zone is put forward in this paper. It provides the reliable basis for developing natural gas through synthetically analyzing the result of carrying on wavelet de-noising and wavelet package denoising disposal simultaneously to the density porosity curve and neutron porosity curve.

The Application of the Wavelet Transform to the Prediction of Gas Zones

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DNA Sequences Classification Based on Wavelet Packet Analysis

The Application of the Wavelet Transform to the Prediction of Gas Zones