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
Virtual genetic coding and time series analysis for alternative splicing prediction in C. elegans
Artificial Intelligence in Medicine
Splice site detection in DNA sequences using a fast classification algorithm
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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
Prediction of alternatively spliced exons using Support Vector Machines
International Journal of Data Mining and Bioinformatics
Support vector machine approach for retained introns prediction using sequence features
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Pattern recognition in bioinformatics: an introduction
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Classification of biological sequences with kernel methods
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
A new classification method for human gene splice site prediction
HIS'12 Proceedings of the First international conference on Health Information Science
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Motivation: Alternative splicing is a major component of the regulatory action on mammalian transcriptomes. It is estimated that over half of all human genes have more than one splice variant. Previous studies have shown that alternatively spliced exons possess several features that distinguish them from constitutively spliced ones. Recently, we have demonstrated that such features can be used to distinguish alternative from constitutive exons. In the current study, we used advanced machine learning methods to generate robust classifier of alternative exons. Results: We extracted several hundred local sequence features of constitutive as well as alternative exons. Using feature selection methods we find seven attributes that are dominant for the task of classification. Several less informative features help to slightly increase the performance of the classifier. The classifier achieves a true positive rate of 50% for a false positive rate of 0.5%. This result enables one to reliably identify alternatively spliced exons in exon databases that are believed to be dominated by constitutive exons. Availability: Upon request from the authors. Contact: gideon@mta.ac.il