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A noisy time series, with both signal and noise varying in frequency and in time, presents special challenges for improving the signal to noise ratio. A modified S-transform time-frequency representation is used to filter a synthetic time series in a two step filtering process. The filter method appears robust within a wide range of background noise levels.
During last few decades accurate determination of protein structural class using a fast and suitable computational method has been a challenging problem in protein science. In this context a meaningful representation of a protein sample plays a key role in achieving higher prediction accuracy. In this paper based on the concept of Chou's pseudo amino acid(More)
Protein-protein interactions govern almost all biological processes and the underlying functions of proteins. The interaction sites of protein depend on the 3D structure which in turn depends on the amino acid sequence. Hence, prediction of protein function from its primary sequence is an important and challenging task in bioinformatics. Identification of(More)
Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still(More)
The time-frequency representation (TFR) has been used as a powerful technique to identify, measure and process the time varying nature of signals. In the recent past S-transform gained a lot of interest in time-frequency localization due to its superiority over all the existing identical methods. It produces the progressive resolution of the wavelet(More)
Predicting the structure of a protein from primary sequence is one of the challenging problems in Molecular biology. In this context, protein structural class information provides a key idea of their structure and also other features related to the biological function. In this paper we present a new optimization approach based on Genetic algorithm (GA) and(More)
— Classification of disease phenotypes using microarray gene expression data faces a critical challenge due to its high dimensionality and small sample size nature. Hence there is a need to develop efficient dimension reduction techniques to improve the class prediction performance. In this paper we present a hybrid feature extraction method to combat the(More)
Prediction of protein function from its sequence is an important and challenging task in Bioinformatics. The biological function of a protein primarily depends on the amino acid sequence within it. Identification of the amino acids (hot spots) that leads to the characteristic frequency signifying a particular biological function is really a tedious job in(More)
—protein structural class prediction has been a challenging problem in protein science for many years. In this paper we present a new optimization approach using the Differential evolution (DE) for predicting the protein structural class. It uses the maximum component coefficient principle in association with the amino acid composition feature vector to(More)