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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
19/01/2011 |
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Data da última atualização: |
03/06/2011 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
SILLA, P. R.; CAMARGO-BRUNETTO, M. A. de O.; BINNECK, E. |
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Afiliação: |
PAULO R. SILLA, UEL; MARIA ANGÉLICA DE O. CAMARGO-BRUNETTO, UEL; ELISEU BINNECK, CNPSO. |
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Título: |
Using a support vector machine to identify Pre-miRNAs in soybean (Glycine max) introns. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2010, Cairo. Proceedings... [Cairo]: IEEE, 2010. CD-ROM. |
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Páginas: |
P. 1235-1241. |
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ISBN: |
978-1-4244-8135-4 |
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Idioma: |
Inglês |
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Conteúdo: |
MicroRNAs (miRNAs) are small Ribonucleic Acid (RNA) molecules ?18?22 nucleotides (nt) in length that regulates gene expression in animals, plants and viruses. Due to its small size and occurrence in different development stages of organisms, the experimental identification of miRNAs becomes difficult, and computational approaches are being developed in order to precede and guide biological experiments. This paper describes our approach based on a Support Vector Machine (SVM) algorithm to identify miRNA?s precursor (pre-miRNA) in soybean (Glycine max) transcript introns, that was developed using a secondary structure predictor of pre-miRNAs sequences to establish the feature set for training, testing and validation phases of SVM algorithm. |
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Palavras-Chave: |
Bioinformática. |
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Categoria do assunto: |
-- |
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Marc: |
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001 1873634
005 2011-06-03
008 2010 bl uuuu u00u1 u #d
020 $a978-1-4244-8135-4
100 1 $aSILLA, P. R.
245 $aUsing a support vector machine to identify Pre-miRNAs in soybean (Glycine max) introns.
260 $aIn: INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2010, Cairo. Proceedings... [Cairo]: IEEE, 2010. CD-ROM.$c2010
300 $aP. 1235-1241.
520 $aMicroRNAs (miRNAs) are small Ribonucleic Acid (RNA) molecules ?18?22 nucleotides (nt) in length that regulates gene expression in animals, plants and viruses. Due to its small size and occurrence in different development stages of organisms, the experimental identification of miRNAs becomes difficult, and computational approaches are being developed in order to precede and guide biological experiments. This paper describes our approach based on a Support Vector Machine (SVM) algorithm to identify miRNA?s precursor (pre-miRNA) in soybean (Glycine max) transcript introns, that was developed using a secondary structure predictor of pre-miRNAs sequences to establish the feature set for training, testing and validation phases of SVM algorithm.
653 $aBioinformática
700 1 $aCAMARGO-BRUNETTO, M. A. de O.
700 1 $aBINNECK, E.
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Registro original: |
Embrapa Soja (CNPSO) |
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