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dc.contributor.authorXie, Bo
dc.contributor.authorJankovic, Boris R.
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorSong, Le
dc.contributor.authorGao, Xin
dc.date.accessioned2014-08-27T09:51:21Z
dc.date.available2014-08-27T09:51:21Z
dc.date.issued2013-6-21
dc.identifier.citationXie B, Jankovic BR, Bajic VB, Song L, Gao X (2013) Poly(A) motif prediction using spectral latent features from human DNA sequences. Bioinformatics 29: i316-i325. doi:10.1093/bioinformatics/btt218.
dc.identifier.issn13674803
dc.identifier.pmid23813000
dc.identifier.doi10.1093/bioinformatics/btt218
dc.identifier.urihttp://hdl.handle.net/10754/325438
dc.description.abstractMotivation: Polyadenylation is the addition of a poly(A) tail to an RNA molecule. Identifying DNA sequence motifs that signal the addition of poly(A) tails is essential to improved genome annotation and better understanding of the regulatory mechanisms and stability of mRNA.Existing poly(A) motif predictors demonstrate that information extracted from the surrounding nucleotide sequences of candidate poly(A) motifs can differentiate true motifs from the false ones to a great extent. A variety of sophisticated features has been explored, including sequential, structural, statistical, thermodynamic and evolutionary properties. However, most of these methods involve extensive manual feature engineering, which can be time-consuming and can require in-depth domain knowledge.Results: We propose a novel machine-learning method for poly(A) motif prediction by marrying generative learning (hidden Markov models) and discriminative learning (support vector machines). Generative learning provides a rich palette on which the uncertainty and diversity of sequence information can be handled, while discriminative learning allows the performance of the classification task to be directly optimized. Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models. These spectral latent features were then fed into support vector machines to fine-tune the classification performance.We evaluated our proposed method on a comprehensive human poly(A) dataset that consists of 14 740 samples from 12 of the most abundant variants of human poly(A) motifs. Compared with one of the previous state-of-the-art methods in the literature (the random forest model with expert-crafted features), our method reduces the average error rate, false-negative rate and false-positive rate by 26, 15 and 35%, respectively. Meanwhile, our method makes ?30% fewer error predictions relative to the other string kernels. Furthermore, our method can be used to visualize the importance of oligomers and positions in predicting poly(A) motifs, from which we can observe a number of characteristics in the surrounding regions of true and false motifs that have not been reported before. The Author 2013.
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0
dc.subjectDNA
dc.subjectpolyadenylic acid
dc.subject3' untranslated region
dc.subjectalgorithm
dc.subjectarticle
dc.subjectartificial intelligence
dc.subjectchemistry
dc.subjectcomputer program
dc.subjectDNA sequence
dc.subjectgenetics
dc.subjecthuman
dc.subjectmethodology
dc.subjectnucleotide motif
dc.subjectpolyadenylation
dc.subjectprobability
dc.subjectsupport vector machine
dc.subject3' Untranslated Regions
dc.subjectAlgorithms
dc.subjectArtificial Intelligence
dc.subjectDNA
dc.subjectHumans
dc.subjectMarkov Chains
dc.subjectNucleotide Motifs
dc.subjectPoly A
dc.subjectPolyadenylation
dc.subjectSequence Analysis, DNA
dc.subjectSoftware
dc.subjectSupport Vector Machines
dc.titlePoly(A) motif prediction using spectral latent features from human DNA sequences
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.identifier.pmcidPMC3694652
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCollege of Computing, Georgia Institute of Technology, Atlanta, GA 30332, United States
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personBajic, Vladimir B.
kaust.personGao, Xin
kaust.personJankovic, Boris R.
refterms.dateFOA2018-06-13T15:23:45Z


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com