ArticleBMC bioinformatics2008
Fast splice site detection using information content and feature reduction.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 35 citations in OpenAlex.
- CNNSplice: Robust models for splice site prediction using convolutional neural networks.Computational and structural biotechnology journal · 2023Article
- EnsembleSplice: ensemble deep learning model for splice site prediction.BMC bioinformatics · 2022Article
- A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples.Biology direct · 2019Article
- Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features.Algorithms for molecular biology : AMB · 2016Article
- Integrated application of uniform design and least-squares support vector machines to transfection optimization.BMC biotechnology · 2009Article
- Emerging strengths in Asia Pacific bioinformatics.BMC bioinformatics · 2008Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate 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.
resultsIn 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's information theory, Shapiro's score scheme, and Markovian probabilities. We also use a feature elimination scheme to reduce the less informative features from the input data.
conclusionIn 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 methods.
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