3DString: a feature string kernel for 3D object classification on voxelized data
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM
Pattern Recognition Letters
An Alternative Splicing Predictor in C.Elegans Based on Time Series Analysis
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Polynomial Summaries of Positive Semidefinite Kernels
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Virtual genetic coding and time series analysis for alternative splicing prediction in C. elegans
Artificial Intelligence in Medicine
Polynomial summaries of positive semidefinite kernels
Theoretical Computer Science
The Feature Importance Ranking Measure
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Improved Online Support Vector Machines Spam Filtering Using String Kernels
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization
The Journal of Machine Learning Research
Advancing the state of the art in computational gene prediction
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A study of spam filtering using support vector machines
Artificial Intelligence Review
Prediction of alternatively spliced exons using Support Vector Machines
International Journal of Data Mining and Bioinformatics
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Motivation: Eukaryotic pre-mRNAs are spliced to form mature mRNA. Pre-mRNA alternative splicing greatly increases the complexity of gene expression. Estimates show that more than half of the human genes and at least one-third of the genes of less complex organisms, such as nematodes or flies, are alternatively spliced. In this work, we consider one major form of alternative splicing, namely the exclusion of exons from the transcript. It has been shown that alternatively spliced exons have certain properties that distinguish them from constitutively spliced exons. Although most recent computational studies on alternative splicing apply only to exons which are conserved among two species, our method only uses information that is available to the splicing machinery, i.e. the DNA sequence itself. We employ advanced machine learning techniques in order to answer the following two questions: (1) Is a certain exon alternatively spliced? (2) How can we identify yet unidentified exons within known introns? Results: We designed a support vector machine (SVM) kernel well suited for the task of classifying sequences with motifs having positional preferences. In order to solve the task (1), we combine the kernel with additional local sequence information, such as lengths of the exon and the flanking introns. The resulting SVM-based classifier achieves a true positive rate of 48.5% at a false positive rate of 1%. By scanning over single EST confirmed exons we identified 215 potential alternatively spliced exons. For 10 randomly selected such exons we successfully performed biological verification experiments and confirmed three novel alternatively spliced exons. To answer question (2), we additionally used SVM-based predictions to recognize acceptor and donor splice sites. Combined with the above mentioned features we were able to identify 85.2% of skipped exons within known introns at a false positive rate of 1%. Availability: Datasets, model selection results, our predictions and additional experimental results are available at http://www.fml.tuebingen.mpg.de/~raetsch/RASE Contact: Gunnar.Raetsch@tuebingen.mpg.de Supplementary information: http://www.fml.tuebingen.mpg.de/raetsch/RASE