Abstract:
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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Zhang W,Zhu X,Fu Y,Tsuji J,Weng Zdoi
10.1186/s12859-017-1875-6subject
Has Abstractpub_date
2017-12-01 00:00:00pages
464issue
Suppl 13issn
1471-2105pii
10.1186/s12859-017-1875-6journal_volume
18pub_type
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