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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: |
04/06/2025 |
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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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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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Thesagro: |
Soja. |
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Thesaurus Nal: |
Artificial intelligence; Soybeans. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
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100 1 $aSILLA, P. R.
245 $aUsing a support vector machine to identify Pre-miRNAs in soybean (Glycine max) introns.$h[electronic resource]
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.
650 $aArtificial intelligence
650 $aSoybeans
650 $aSoja
653 $aBioinformática
700 1 $aCAMARGO-BRUNETTO, M. A. de O.
700 1 $aBINNECK, E.
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Embrapa Soja (CNPSO) |
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| 2. |  | PINTO, P. H. N.; FREITAS, J. A.; FONSECA, J. D.; PILE, E. A.; ESTEVES, L. V.; SOUZA, V. L.; SOUZA-FABJAN, J. M. G.; FONSECA, J. F. da. Artificial insemination in Cape Verdean goats with cooled semen stored for 24 or 48 Hours. Australian Journal of Basic and Applied Sciences, v. 8, n. 18, p. 473-478, Dec. 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
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