Evaluation of Weight Matrix Models in the splice junction recognition problem

Tavares, Leonardo G.; Lopes, Heitor Silvério; Lima, Carlos R. Erig

doi:10.1109/bibmw.2009.5332130

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…MSC = multi-scale component; Pos = position; APR = adjacent position relationship; SVM = support vector machine; Sn = sensitivity; Sp = specificity; Mcc = Matthew's correlation coefficients. Compared to SVM+B and MM1-SVM from Zhang et al (2010) and MDD/WWAM from Tavares et al (2009), our method gave a better performance. For donor sites, our MSC+Pos+APR model gave the best prediction with an Mcc of 0.922 m, which was 0.068 higher than that of SVM+B and 0.082 higher than that of MDD/WWAM.…”

Section: Parameter Optimization Based On Apr Featuresmentioning

confidence: 99%

“…For donor sites, the optimal model was the one with integrated MSC, Pos and APR, and its Mcc was 0.922, but for acceptor sites, the optimal model was the one with integrated MSC and Pos, which had an Mcc of 0.887. SVM+B denotes the prediction method using SVM with a Bayes kernel; MM1-SVM is a prediction method that used probabilistic parameters and SVM classifier (Zhang et al, 2010), and MDD/WWAM denotes the method using maximum dependence decomposition and windowed weight array model (Tavares et al, 2009). MSC = multi-scale component; Pos = position; APR = adjacent position relationship; SVM = support vector machine; Sn = sensitivity; Sp = specificity; Mcc = Matthew's correlation coefficients.…”

Section: Parameter Optimization Based On Apr Featuresmentioning

confidence: 99%

“…The weight matrix method (WMM) is the earliest and most influential one that uses the position-specific compositional biases (Staden, 1984;Tavares et al, 2009). Subsequently, the pattern recognition algorithms, represented by a Bayesian network (Cai et al, 2000), support vector machine (SVM) (Zhang et al, 2006;Baten et al, 2006;Sonnenburg et al, 2007;Asa et al, 2008;Zhang et al, 2009), hidden Markov model (Baten et al, 2007(Baten et al, , 2008Asa et al, 2008;Zhang et al, 2009Zhang et al, , 2010, artificial neural network (Reese et al, 1997;Wang et al, 2009), etc., were successively introduced.…”

Section: Introductionmentioning

confidence: 99%

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High-accuracy splice site prediction based on sequence component and position features

Wang²,

Wang³

et al. 2012

Genet. Mol. Res.

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ABSTRACT. Identification of splice sites plays a key role in the annotation of genes. Consequently, improvement of computational prediction of splice sites would be very useful. We examined the effect of the window size and the number and position of the consensus bases with a chi-square test, and then extracted the sequence multi-scale component features and the position and adjacent position relationship features of consensus sites. Then, we constructed a novel classification model using a support vector machine with the previously selected features and applied it to the Homo sapiens splice site dataset. This method greatly improved cross-validation accuracies for training sets with true and spurious splice sites of both equal and different proportions. This method was also applied to the NN269 dataset for further evaluation and independent testing. The results were superior to those obtained with previous methods, and demonstrate the stability and superiority of this method for prediction of splice sites.

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Section: Parameter Optimization Based On Apr Featuresmentioning

confidence: 99%

Section: Parameter Optimization Based On Apr Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-accuracy splice site prediction based on sequence component and position features

Wang²,

Wang³

et al. 2012

Genet. Mol. Res.

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“…Eukaryotic genes are composed of two types of segments: exons and introns; while the exons are regions of the genetic material that are encoded proteins, on the other hand the introns are non-coding regions that are removed from the primary transcript [3]. The intron-exon boundary is referred to as acceptor splice site and the exon-intron boundary as donor splice site [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Artificial Neural Network and an Improved Particle Swarm Optimization used in Splice Site Prediction

Jing¹

2014

J Appl Computat Math

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The amount of DNA sequence data produced by several genomic projects had increased dramatically in recent years. One of the main goals of bioinformatics was to identify genes. A crucial part of the gene identification was to precisely detect the exon intron boundaries, i.e. the splice sites. This paper introduced a new type of artificial neural network (called NANN), which was designed specifically to solve the splice sites prediction problem. Moreover, the network connection weights of NANN were determined by an improved particle swarm optimization which was inspired by the wolves' activities circle. In addition, three types of encoding approaches were applied to generate the input for the NANN. Intensive experiments were presented in this paper, and the results showed that our algorithm was better than some current methods, that is, the NANN_IPSO was applicable to splice site prediction problem.

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“…Position weight matrix (PWM) is a common model for splice site prediction [2], [3], [4]. The varieties of PWMs have been used for splice site prediction such as Weight Array Models [5] and Windowed Weight Array Model [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Accuracy of Splice Site Prediction based on Integrating Jensen-Shannon Divergence and a Polynomial Equation of Order 2

Seyfari¹,

Didehvar²,

Banaee³

et al. 2016

IJCA

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Advances in DNA sequencing technology have caused generation of the vast amount of new sequence data. It is essential to understand the functions, features, and structures of every newly sequenced data. Analyzing sequence data by different methods could provide important information about the sequence data. One of the essential tasks for genome annotation is gene prediction that can help to understand the features and determine functions of the genes. One of the key steps towards correct gene structure prediction is accurate splice site detection. There are vast numbers of splice site prediction methods, however, a few of them can be incorporated in gene prediction modules because of their complexity. In this paper, a novel model is presented to recognize unknown splice sites in a new genome without using any prior knowledge. Our model is defined based on integrating Jensen-Shannon divergence and a polynomial equation of order 2. Finally, the proposed model is evaluated on Yeast's genome to predict splice sites. The experimental results suggest that the proposed method is an effective approach for splice site prediction.

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Evaluation of Weight Matrix Models in the splice junction recognition problem

Cited by 3 publications

References 22 publications

High-accuracy splice site prediction based on sequence component and position features

High-accuracy splice site prediction based on sequence component and position features

A Novel Artificial Neural Network and an Improved Particle Swarm Optimization used in Splice Site Prediction

Evaluating the Accuracy of Splice Site Prediction based on Integrating Jensen-Shannon Divergence and a Polynomial Equation of Order 2

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