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Prediction of small non-coding RNA in bacterial genomes using support vector machines

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Title: Prediction of small non-coding RNA in bacterial genomes using support vector machines
Authors: Chang, Tzu-Hao1, Wu, Li-Ching2, Lin, Jun-Hong1, Huang, Hsien-Da3, Liu, Baw-Jhiune4, Cheng, Kuang-Fu5, Horng, Jorng-Tzong1,2,6 horng@db.csie.ncu.edu.tw
Source: Expert Systems with Applications. Aug2010, Vol. 37 Issue 8, p5549-5557. 9p.
Subjects: Prediction models, Expert systems, Support vector machines, Non-coding RNA, Bioinformatics, Machine learning, Bacterial genomes, Escherichia coli
Abstract: Abstract: Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes. [Copyright &y& Elsevier]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 50358199
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Prediction of small non-coding RNA in bacterial genomes using support vector machines
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  Data: <searchLink fieldCode="AR" term="%22Chang%2C+Tzu-Hao%22">Chang, Tzu-Hao</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Wu%2C+Li-Ching%22">Wu, Li-Ching</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Lin%2C+Jun-Hong%22">Lin, Jun-Hong</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Huang%2C+Hsien-Da%22">Huang, Hsien-Da</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Liu%2C+Baw-Jhiune%22">Liu, Baw-Jhiune</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Kuang-Fu%22">Cheng, Kuang-Fu</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Horng%2C+Jorng-Tzong%22">Horng, Jorng-Tzong</searchLink><relatesTo>1,2,6</relatesTo><i> horng@db.csie.ncu.edu.tw</i>
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Aug2010, Vol. 37 Issue 8, p5549-5557. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Expert+systems%22">Expert systems</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Non-coding+RNA%22">Non-coding RNA</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bacterial+genomes%22">Bacterial genomes</searchLink><br /><searchLink fieldCode="DE" term="%22Escherichia+coli%22">Escherichia coli</searchLink>
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  Data: Abstract: Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.eswa.2010.02.058
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      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 5549
    Subjects:
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Expert systems
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Non-coding RNA
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      – SubjectFull: Bioinformatics
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Bacterial genomes
        Type: general
      – SubjectFull: Escherichia coli
        Type: general
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      – TitleFull: Prediction of small non-coding RNA in bacterial genomes using support vector machines
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              Text: Aug2010
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              Y: 2010
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