We introduce a new method for splicing sites prediction based on the theory of support vector machines (SVM). The SVM represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In the process of splicing sites prediction, the statistical information of RNA secondary structure in the vicinity of splice sites, e.g. donor and acceptor sites, is introduced in order to compare recognition ratio of true positive and true negative. From the results of comparison, addition of structural information has brought no significant benefit for the recognition of splice sites and had even lowered the rate of recognition. Our results suggest that, through three cross validation, the SVM method can achieve a good performance for splice sites identi7cation.

Biological evolution gives rise to self-organizing phenomena. Inspired by this theory, directed variation is added to the (μ,λ) evolution strategies (ES) algorithm and it is called directed variation ES (DVES). In DVES, some neighboring individuals in the population mutate correlatively according to the distribution of the whole population. Experimental results showed that, with the same number of function evaluations, directed variation ES reached better optimization results for different generally used strategies under the ES framework. Experimental analysis showed that the application of directed variation could increase the expected fitness improvement and the probability of fitness improvement. From a biological perspective, directed variation can be regarded as a result of self-organizing evolution.

Abstract-Few analytical models have been proposed to evaluate the hybrid of the basic and the RTS/CTS access mechanisms up to now. The main reason is that it is very difficult to study a scheme with variable packet length. In this letter, we propose a model to evaluate the saturation throughput performance of the hybrid access mechanism, assuming that the packet lengths are sampled from a general distribution function f(χ). Index Terms-IEEE 802.11, performance model, WLAN.

In this paper some parametric methods are introduced to characterize the self-similarity of DNA sequences. Compared with Fourier analysis, these methods perform statistically more stably and yield more reliable results. Using these methods, eight whole genomes of bacteria provided by NCBI are analyzed. Long-range correlation properties in the nucleotide density distribution along these DNA sequences are explored. Estimation results show that the long-range correlation structure prevails through the entire molecule of DNA. Higher order statistics through coarse graining reveal that rather than multifractal, there are only monofractal phenomena presented in the sequences. Hence, the nucleotide density distribution can be modeled asymptotically as fractional Gaussian noise. This result points to a new direction for analyzing and understanding the intrinsic structures of DNA sequences.

Motivation: Protein localization data are a valuable information resource helpful in elucidating protein functions. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Results: In this paper, fuzzy k-nearest neighbors (k-NN) algorithm has been introduced to predict proteins' subcellular locations from their dipeptide composition. The prediction is performed with a new data set derived from version 41.0 SWISS-PROT databank, the overall predictive accuracy about 80% has been achieved in a jackknife test. The result demonstrates the applicability of this relative simple method and possible improvement of prediction accuracy for the protein subcellular locations. We also applied this method to annotate six entirely sequenced proteomes, namely Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Oryza sativa, Arabidopsis thaliana and a subset of all human proteins.