Correction: Splice site identification using probabilistic parameters and SVM classification

BACKGROUND: Recent advances and automation in DNA sequencing technology has created a vast amount of DNA sequence data. This increasing growth of sequence data demands better and efficient analysis methods. Identifying genes in this newly accumulated data is an important issue in bioinformatics, and it requires the prediction of the complete gene structure. Accurate identification of splice sites in DNA sequences plays one of the central roles of gene structural prediction in eukaryotes. Effective detection of splice sites requires the knowledge of characteristics, dependencies, and relationship of nucleotides in the splice site surrounding region. A higher-order Markov model is generally regarded as a useful technique for modeling higher-order dependencies. However, their implementation requires estimating a large number of parameters, which is computationally expensive. RESULTS: The proposed method for splice site detection consists of two stages: a first order Markov model (MM1) is used in the first stage and a support vector machine (SVM) with polynomial kernel is used in the second stage. The MM1 serves as a pre-processing step for the SVM and takes DNA sequences as its input. It models the compositional features and dependencies of nucleotides in terms of probabilistic parameters around splice site regions. The probabilistic parameters are then fed into the SVM, which combines them nonlinearly to predict splice sites. When the proposed MM1-SVM model is compared with other existing standard splice site detection methods, it shows a superior performance in all the cases. CONCLUSION: We proposed an effective pre-processing scheme for the SVM and applied it for the identification of splice sites. This is a simple yet effective splice site detection method, which shows a better classification accuracy and computational speed than some other more complex methods

Fast splice site detection using information content and feature reduction

Background: Accurate identification of splice sites in DNA sequences plays a key role in the prediction of gene structure in eukaryotes. Already many computational methods have been proposed for the detection of splice sites and some of them showed high prediction accuracy. However, most of these methods are limited in terms of their long computation time when applied to whole genome sequence data. Results: In this paper we propose a hybrid algorithm which combines several effective and informative input features with the state of the art support vector machine (SVM). To obtain the input features we employ information content method based on Shannon\u27s information theory, Shapiro\u27s score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data. Conclusion: In this study we propose a new feature based splice site detection method that shows improved acceptor and donor splice site detection in DNA sequences when the performance is compared with various state of the art and well known method

Rigorous analysis of numerical phase and group velocity bounds in Yee's FDTD grid

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Face localisation for driver fatigue recognition

IEEE International Conference on Information and Automation (ICIA

Polynomial kernel adaptation and extensions to the SVM classifier learning

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Hierarchical and Interpretable Connectionist Structure Generation from Data

4

Optimized rule-based delay proportion adjustment-for proportional differentiated services

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An optimized anti-lock braking system in the presence of multiple road surface types

In this paper, the use of adaptive anti-lock braking system (A-ABS) comprising of a road surface identification (RSID) system and road surface information modules is presented. The proposed ABS system is capable of identifying and differentiating different types of road surfaces, and applying an amount of brake force appropriate to the road surface type being encountered in order to prevent wheel lockup as well as to minimize the braking distance. A discriminative hierarchical evolutionary fuzzy system learns and identifies on-the-fly the road surface characteristics, from a set of built-in road surface information modules, in a closed-loop adaptive configuration. The closed-loop nature of RSID allows the system to adapt and respond very fast to a sudden change in surface condition. In order to verify the performance of the proposal, simulation results obtained from cars equipped with A-ABS, a reference ABS, and a non-ABS are provided and discussed

Structure adaptation of hierarchical knowledge-based classifiers

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