UMMS Affiliation

Program in Bioinformatics and Integrative Biology; Department of Biochemistry and Molecular Pharmacology

Publication Date


Document Type



Bioinformatics | Computational Biology | Genetic Phenomena | Genetics and Genomics | Integrative Biology


BACKGROUND: Alternative splicing is the critical process in a single gene coding, which removes introns and joins exons, and splicing branchpoints are indicators for the alternative splicing. Wet experiments have identified a great number of human splicing branchpoints, but many branchpoints are still unknown. In order to guide wet experiments, we develop computational methods to predict human splicing branchpoints.

RESULTS: Considering the fact that an intron may have multiple branchpoints, we transform the branchpoint prediction as the multi-label learning problem, and attempt to predict branchpoint sites from intron sequences. First, we investigate a variety of intron sequence-derived features, such as sparse profile, dinucleotide profile, position weight matrix profile, Markov motif profile and polypyrimidine tract profile. Second, we consider several multi-label learning methods: partial least squares regression, canonical correlation analysis and regularized canonical correlation analysis, and use them as the basic classification engines. Third, we propose two ensemble learning schemes which integrate different features and different classifiers to build ensemble learning systems for the branchpoint prediction. One is the genetic algorithm-based weighted average ensemble method; the other is the logistic regression-based ensemble method.

CONCLUSIONS: In the computational experiments, two ensemble learning methods outperform benchmark branchpoint prediction methods, and can produce high-accuracy results on the benchmark dataset.


Genetic algorithm, Human splicing branchpoint, Logistic regression, Multi-label learning

Rights and Permissions

© The Author(s). 2017. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

DOI of Published Version



BMC Bioinformatics. 2017 Dec 1;18(Suppl 13):464. doi: 10.1186/s12859-017-1875-6. Link to article on publisher's site

Journal/Book/Conference Title

BMC bioinformatics

Related Resources

Link to Article in PubMed

PubMed ID


Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.



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