ArticleBMC bioinformatics2007
Accurate splice site prediction using support vector machines.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- Splam: a deep-learning-based splice site predictor that improves spliced alignments.Genome biology · 2024Article
- DRANetSplicer: A Splice Site Prediction Model Based on Deep Residual Attention Networks.Genes · 2024Article
- Splam: a deep-learning-based splice site predictor that improves spliced alignments.bioRxiv : the preprint server for biology · 2023Article
- Deep learning and support vector machines for transcription start site identification.PeerJ. Computer science · 2023Article
- EnsembleSplice: ensemble deep learning model for splice site prediction.BMC bioinformatics · 2022Article
- Spliceator: multi-species splice site prediction using convolutional neural networks.BMC bioinformatics · 2021Article
- Improved recognition of splice sites in3 Biotech · 2021Article
- MTSplice predicts effects of genetic variants on tissue-specific splicing.Genome biology · 2021Article
- DASSI: differential architecture search for splice identification from DNA sequences.BioData mining · 2021Article
- Learning the Regulatory Code of Gene Expression.Frontiers in molecular biosciences · 2021Review
- Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.Gene: X · 2020Article
- Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Machine learning: A powerful tool for gene function prediction in plants.Applications in plant sciences · 2020Review
- Epigenome-based splicing prediction using a recurrent neural network.PLoS computational biology · 2020Article
- Article
- SpliceFinder: ab initio prediction of splice sites using convolutional neural network.BMC bioinformatics · 2019Article
- A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples.Biology direct · 2019Article
- Discerning novel splice junctions derived from RNA-seq alignment: a deep learning approach.BMC genomics · 2018Article
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5 authors.
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Abstract
backgroundFor splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks.
resultsIn this work we consider Support Vector Machines for splice site recognition. We employ the so-called weighted degree kernel which turns out well suited for this task, as we will illustrate in several experiments where we compare its prediction accuracy with that of recently proposed systems. We apply our method to the genome-wide recognition of splice sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, and Homo sapiens. Our performance estimates indicate that splice sites can be recognized very accurately in these genomes and that our method outperforms many other methods including Markov Chains, GeneSplicer and SpliceMachine. We provide genome-wide predictions of splice sites and a stand-alone prediction tool ready to be used for incorporation in a gene finder. AVAILABILITY: Data, splits, additional information on the model selection, the whole genome predictions, as well as the stand-alone prediction tool are available for download at http://www.fml.mpg.de/raetsch/projects/splice.
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