Predicting alternatively spliced exons using semi-supervised learning. (2016)

• Record Type: Journal Article
• Authors: Stanescu, Ana; Tangirala, Karthik; Caragea, Doina
• Is Part Of: International journal of data mining and bioinformatics. Volume 14:Number 1(2016)
• Page Start: 1
• Page End: 21
• Publication Date: 2016
• Subjects: semi-supervised learning -- expectation maximisation -- self-training -- co-training -- alternatively spliced exons -- constitutively spliced exons -- ROC -- receiver operating characteristic -- parameter tuning -- cross-validation -- Caenorhabditis elegans -- unlabelled data -- latent information -- DNA sequences -- sequence annotation -- alternative splicing -- bioinformatics; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Cost-efficient next generation sequencers can now produce unprecedented volumes of raw DNA data, posing challenges for annotation. Supervised machine learning approaches have been traditionally used to analyse and annotate complex genomic information. However, such approaches require labelled data for training, which in practice is scarce or expensive, while the unlabelled data is abundant. For some problems, semi-supervised learning can help improve supervised classifiers by making use of large amounts of unlabelled data and the latent information within them. We evaluate the applicability of semi-supervised learning algorithms to the problem of DNA sequence annotation, specifically to the prediction of alternatively spliced exons. We employ Expectation Maximisation, Self-training, and Co-training algorithms in an effort to assess the strengths and limitations of these techniques in the context of alternative splicing.