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Biblioteca(s): |
Embrapa Soja. |
Data corrente: |
30/05/2016 |
Data da última atualização: |
26/07/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
LOPES, I. de O. N.; SCHLIEP, A.; CARVALHO, A. P. de L. F. de. |
Afiliação: |
IVANI DE OLIVEIRA NEGRAO LOPES, CNPSO; ALEXANDER SCHLIEP, Rutgers University, USA; ANDRÉ P. DE L. F. de CARVALHO, Instituto de Ciências Matemáticas e de Computação, São Carlos. |
Título: |
Automatic learning of pre-miRNAs from different species. |
Ano de publicação: |
2016 |
Fonte/Imprenta: |
BMC Bioinformatics, v. 17, n. 224, 18 p., 2016. |
ISSN: |
1471-2105 |
DOI: |
10.1186/s12859-016-1036-3 |
Idioma: |
Português |
Conteúdo: |
Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools. MenosDiscovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers ... Mostrar Tudo |
Palavras-Chave: |
Bioinformática. |
Thesagro: |
Automação; Biologia. |
Thesaurus Nal: |
Bioinformatics; Biological Sciences. |
Categoria do assunto: |
S Ciências Biológicas |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/143864/1/Automatic-learning-of-pre-miRNAs-from-different-species.pdf
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Marc: |
LEADER 02586naa a2200229 a 4500 001 2045860 005 2017-07-26 008 2016 bl uuuu u00u1 u #d 022 $a1471-2105 024 7 $a10.1186/s12859-016-1036-3$2DOI 100 1 $aLOPES, I. de O. N. 245 $aAutomatic learning of pre-miRNAs from different species.$h[electronic resource] 260 $c2016 520 $aDiscovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools. 650 $aBioinformatics 650 $aBiological Sciences 650 $aAutomação 650 $aBiologia 653 $aBioinformática 700 1 $aSCHLIEP, A. 700 1 $aCARVALHO, A. P. de L. F. de 773 $tBMC Bioinformatics$gv. 17, n. 224, 18 p., 2016.
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Embrapa Soja (CNPSO) |
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Biblioteca(s): |
Embrapa Tabuleiros Costeiros. |
Data corrente: |
26/03/2019 |
Data da última atualização: |
26/03/2019 |
Tipo da produção científica: |
Resumo em Anais de Congresso |
Autoria: |
NASCIEMENTO, S. L. S.; ASSUNCAO, D. A. de; MUNIZ, E. N.; MUNIZ, A. V. C. da S. |
Afiliação: |
ANA LETICIA SIRQUEIRA NASCIMENTO; DANIELA ALMEIDA DE ASSUNCAO; EVANDRO NEVES MUNIZ, CPATC; ANA VERUSKA CRUZ DA SILVA MUNIZ, CPATC. |
Título: |
Desenvolvimento de acessos de nim indiano. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Revista RG News, v. 4, n. 3, p. 277, 2018. |
Idioma: |
Português |
Notas: |
Edição especial dos Anais do 5º Congresso Brasileiro de Recursos Genéticos, Fortaleza, nov. 2018. |
Palavras-Chave: |
Descritor morfológico. |
Thesagro: |
Nim. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/195010/1/Desenvolvimento.pdf
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Marc: |
LEADER 00579nam a2200169 a 4500 001 2107493 005 2019-03-26 008 2018 bl uuuu u00u1 u #d 100 1 $aNASCIEMENTO, S. L. S. 245 $aDesenvolvimento de acessos de nim indiano.$h[electronic resource] 260 $aRevista RG News, v. 4, n. 3, p. 277$c2018 500 $aEdição especial dos Anais do 5º Congresso Brasileiro de Recursos Genéticos, Fortaleza, nov. 2018. 650 $aNim 653 $aDescritor morfológico 700 1 $aASSUNCAO, D. A. de 700 1 $aMUNIZ, E. N. 700 1 $aMUNIZ, A. V. C. da S.
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