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Conference Paper: A novel profile hidden Markov model to predict microRNAs and their targets simultaneously

TitleA novel profile hidden Markov model to predict microRNAs and their targets simultaneously
Authors
Issue Date2010
Citation
The 9th Annual International Conference on Computational Systems Bioinformatics (CSB), Stanford, CA., 16-18 August 2010. How to Cite?
AbstractThe microRNAs (miRNAs) are small non-coding RNAs of 22 nucleotides, which play important roles in gene regulation. By binding to 3’ UTR of the target mRNA, miRNAs can either speed up the degradation of mRNAs or slow down the translation of gene products. The identification of miRNA and its functional target sites becomes an essential issue. Although existing computational approaches have made significant progresses in predicting either miRNAs or targets, the underlying mechanism of miRNA-target interaction still remained largely unknown. To further understand this interaction, here we present a novel Profile Hidden Markov Model which can predict miRNAs and their targets simultaneously. Unlike current strategies which only use features from a group of miRNAs or a set of targets for one particular miRNA, our model integrates the features from both miRNA and target together and thus captured evolutionary relationships of both. Since the “seed region” is only 8bp long, current models based on region like this short inevitably suffer from high false positive rate. Besides, recent studies also reported the prevalence of interactions beyond seed matching as supplementary binding. Our method provides an unbiased way to model the interaction. By considering miRNA and target features together, we double the size of modeling region to 22×2. As more and more miRNA-target pairs are discovered, this HMM could achieve higher specificity. In preliminary tests, it outperformed RNAhybrid and achieved satisfactory results for leave-one-out cross validation.
DescriptionPoster presentations
Persistent Identifierhttp://hdl.handle.net/10722/129611

 

DC FieldValueLanguage
dc.contributor.authorYang, Sen_US
dc.contributor.authorAgrawal, Ken_US
dc.contributor.authorLam, TWen_US
dc.contributor.authorSham, PCen_US
dc.contributor.authorCheah, Ken_US
dc.contributor.authorWang, Jen_US
dc.date.accessioned2010-12-23T08:40:02Z-
dc.date.available2010-12-23T08:40:02Z-
dc.date.issued2010en_US
dc.identifier.citationThe 9th Annual International Conference on Computational Systems Bioinformatics (CSB), Stanford, CA., 16-18 August 2010.-
dc.identifier.urihttp://hdl.handle.net/10722/129611-
dc.descriptionPoster presentations-
dc.description.abstractThe microRNAs (miRNAs) are small non-coding RNAs of 22 nucleotides, which play important roles in gene regulation. By binding to 3’ UTR of the target mRNA, miRNAs can either speed up the degradation of mRNAs or slow down the translation of gene products. The identification of miRNA and its functional target sites becomes an essential issue. Although existing computational approaches have made significant progresses in predicting either miRNAs or targets, the underlying mechanism of miRNA-target interaction still remained largely unknown. To further understand this interaction, here we present a novel Profile Hidden Markov Model which can predict miRNAs and their targets simultaneously. Unlike current strategies which only use features from a group of miRNAs or a set of targets for one particular miRNA, our model integrates the features from both miRNA and target together and thus captured evolutionary relationships of both. Since the “seed region” is only 8bp long, current models based on region like this short inevitably suffer from high false positive rate. Besides, recent studies also reported the prevalence of interactions beyond seed matching as supplementary binding. Our method provides an unbiased way to model the interaction. By considering miRNA and target features together, we double the size of modeling region to 22×2. As more and more miRNA-target pairs are discovered, this HMM could achieve higher specificity. In preliminary tests, it outperformed RNAhybrid and achieved satisfactory results for leave-one-out cross validation.-
dc.languageengen_US
dc.relation.ispartofAnnual International Conference on Computational Systems Bioinformatics-
dc.titleA novel profile hidden Markov model to predict microRNAs and their targets simultaneouslyen_US
dc.typeConference_Paperen_US
dc.identifier.emailYang, S: syang.2008@hku.hken_US
dc.identifier.emailAgrawal, K: agrawal.kalpana1@gmail.comen_US
dc.identifier.emailLam, TW: twlam@cs.hku.hken_US
dc.identifier.emailSham, PC: pcsham@HKUCC.hku.hken_US
dc.identifier.emailCheah, K: hrmbdkc@hkusua.hku.hken_US
dc.identifier.emailWang, J: junwen2u@gmail.comen_US
dc.identifier.hkuros178418en_US

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