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Identification of protein coding regions (exons) in eukaryotic genomic sequences is an active area of research at present. Mapping of symbolic genomic sequences to numeric sequences is the first step required for processing them using digital signal processing (DSP) tools. For DFT-based methods paired numeric and frequency of nucleotide are reported as the best mapping schemes. In this work performance...
The accuracy of methods based on power spectrum analysis depends on the threshold that is used to discriminate the coding and non-coding sequences. Due to gene structural differences of different organisms, we inferred that there is an optimal gene prediction threshold for each organism. To prove this, we analyzed real biological data, and found that there are indeed different optimal thresholds for...
In this paper, we develop a new method for prediction O-linked glycosylation site and pattern analysis in protein, which combines independent component analysis (ICA) with a multi-layer neural network (NN). ICA is first used to construct main basis (subspace) of the protein sequence for features extraction. The projections of protein sequence on the subspace with low dimension are used as input data...
This paper addresses the problem of distinguishing retroviruses from non-coding DNA sequences. Retroviruses have a distinctive reading frame structure that includes multiple reading frames that often overlap. This paper uses reading frame information generated from Fourier spectral analysis as input for Side Effect Machines (SEMs) that are evolved to create clusterings which separate the two types...
Influenza viruses continue to evolve rapidly and are responsible for seasonal epidemics and occasional, but catastrophic, pandemics. We recently demonstrated the use of decision tree and support vector machine methods in classifying pandemic swine flu viral strains with high accuracy. Here, we applied the technique of artificial neural networks for the prediction of important influenza virus antigenic...
Next generation sequencing is quickly changing long standing paradigms of genomics in terms of what is feasible to accomplish within a ldquoresearch life timerdquo and what is supposed to remain beyond limits of reliable experimental analysis. Sequencing and mapping of a prokaryote transcriptome can provide experimental validation for computationally predicted genes annotated in a prokaryotic genome...
Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome. We propose a multiobjective methodology to combine algorithms into an aggregation scheme in order to obtain optimal methods'...
Overlap decomposition is one of the difficulties in spike sorting. A method based on genetic algorithm is proposed to deal with the overlapped signal from two single waves in this paper. Specifically, with the reliable signal wave templates given, the methods for overlap signals and for single wave signals are used simultaneously to process the unknown data from the signal acquisition equipment, and...
Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes. The prediction of interesting proteins types by computational methods can provide new clues in functional studies of uncharacterized proteins without performing extensive experiments. Support vector machine (SVM) is a new kind of approach to supervised pattern classification...
Based on neural network, an improvement scheme that iterative matrix replace secondary derivative has been developed by introduced quasi-Newton algorithm. Profile code based on probability has been used and comparison of window width and learning training has been completed. The experiment results indicate that the prediction for secondary structures of protein obtain a very good effect based on neural...
The support vector machine (SVM) method based on n-peptide composition (Yu et al, Proteins: Struct. Funct. Genet. 2003:50:531-536) is used to predict the subcellular localizations of proteins. For an unbiased assessment of the results, we apply our approach to two independent data sets: one set consisting of two parts (Reinhardt and Hubbard, Nucleic Acids Res. 1998; 26:2230-2236): the prokaryotic...
Due to the enormous amount of data in DNA sequences to be processed, the computational complexity and speed are important issues to be considered. In this paper, a new integrative method is presented for predicting protein coding regions. We first establish a Takagi-Sugeno fuzzy model to identify the first nucleotide of a codon in coding regions, then the time-frequency characteristics of the output...
Protein methylation is one important type of post-translational modifications of proteins. Experimentally identifying methylation positions in protein sequences is time-consuming and costly. In order to provide insightful advice and reduce cost for further experiments, we propose a novel granular decision fusion framework based on granular computing, computational intelligence, and statistical learning...
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