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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
25/04/2018 |
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Data da última atualização: |
25/04/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LOPES, I. de O. N.; SCHLIEP, A.; CARVALHO, A. C. P. de L. F. de. |
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Afiliação: |
IVANI DE OLIVEIRA NEGRAO LOPES, CNPSO; ALEXANDER SCHLIEP; ANDRÉ C. P. de L. F de CARVALHO. |
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Título: |
The discriminant power of RNA features for pre-miRNA recognition. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
BMC Bioinformatics, London, v. 15, p.124, 2014. |
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ISSN: |
1471-2105 |
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Idioma: |
Inglês |
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Conteúdo: |
Computational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. Small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Inspired in these results, we obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. These estimates are within 0.1% of the maximal values obtained with any feature set (SELECT, Section? Results and discussion?) while it is 34 times faster to compute. Even compared to another feature set (FS2, see Section? Results and discussion?), which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34 times faster to compute. The results obtained by the tools used as references in the experiments carried out showed that five out of these six tools have lower sensitivity or specificity. In miRNA discovery the number of putative miRNA loci is in the order of millions. Analysis of putative pre-miRNAs using a computationally expensive feature set would be wasteful or even unfeasible for large genomes. In this work, we propose a relatively inexpensive feature set and explore most of the learning aspects implemented in current ab-initio pre-miRNA prediction tools, which may lead to the development of efficient ab-initio pre-miRNA discovery tools. The material to reproduce the main results from this paper can be downloaded from http://bioinformatics.rutgers. edu/Static/Software/discriminant.tar.gz MenosComputational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. Small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Inspired in these results, we obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. These estimates are within 0.1% of the maximal values obtained with any feature set (SELECT, Section? Results and discussion?) while it is 34 times faster to compute. Even compared to another feature set (FS2, see Section? Results and discussion?), which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34 times faster to compute. The results obtained by the tools used as references in the experiments c... Mostrar Tudo |
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Palavras-Chave: |
Bioinformática. |
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Categoria do assunto: |
-- |
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Marc: |
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100 1 $aLOPES, I. de O. N.
245 $aThe discriminant power of RNA features for pre-miRNA recognition.$h[electronic resource]
260 $c2014
520 $aComputational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. Small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Inspired in these results, we obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. These estimates are within 0.1% of the maximal values obtained with any feature set (SELECT, Section? Results and discussion?) while it is 34 times faster to compute. Even compared to another feature set (FS2, see Section? Results and discussion?), which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34 times faster to compute. The results obtained by the tools used as references in the experiments carried out showed that five out of these six tools have lower sensitivity or specificity. In miRNA discovery the number of putative miRNA loci is in the order of millions. Analysis of putative pre-miRNAs using a computationally expensive feature set would be wasteful or even unfeasible for large genomes. In this work, we propose a relatively inexpensive feature set and explore most of the learning aspects implemented in current ab-initio pre-miRNA prediction tools, which may lead to the development of efficient ab-initio pre-miRNA discovery tools. The material to reproduce the main results from this paper can be downloaded from http://bioinformatics.rutgers. edu/Static/Software/discriminant.tar.gz
653 $aBioinformática
700 1 $aSCHLIEP, A.
700 1 $aCARVALHO, A. C. P. de L. F. de
773 $tBMC Bioinformatics, London$gv. 15, p.124, 2014.
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Registro original: |
Embrapa Soja (CNPSO) |
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| Registros recuperados : 14 | |
| 3. |  | SILVA, M. J. da; SILVA JÚNIOR, A. C. da; CRUZ, C. D.; NASCIMENTO, M.; OLIVEIRA, M. da S.; SCHAFFERT, R. E.; PARRELLA, R. A. da C. Computational intelligence for studies on genetic diversity between genotypes of biomass sorghum. Pesquisa Agropecuária Brasileira, v. 55, e01723, 2020. Título em português: Inteligência computacional para estudos de diversidade genética entre genótipos de sorgo biomassa.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Milho e Sorgo; Embrapa Unidades Centrais. |
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| 4. | | SOARES, P. C.; COSTA, W. G. da; SILVA JUNIOR, A. C. da; CORNÉLIO V. M. de O.; REIS, M. de S.; MORAIS, O. P. de. BRSMG Rubelita: cultivar de arroz para cultivo em várzeas mineiras. In: CONGRESSO BRASILEIRO DE MELHORAMENTO DE PLANTAS, 8., 2015, Goiânia. O melhoramento de plantas, o futuro da agricultura e a soberania nacional: anais. Goiânia: UFG: SBMP, 2015.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 7. | | SOARES, P. C.; GONÇALVES, R. de P.; COSTA, W. G. da; SILVA JÚNIOR, A. C. da; REIS, M. de S.; CONDÉ, A. B. T.; TORGA, P. P. Ensaio comparativo preliminar de arroz irrigado em Minas Gerais - safra 2018/2019. In: CONGRESSO BRASILEIRO DE ARROZ IRRIGADO, 11., 2019. Balneário Camboriú, SC. Inovação e desenvolvimento na orizicultura: anais eletrônico. Itajaí: Epagri: Sosbai, 2019.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 8. | | COSTA, W. G. da; SANTOS, I. G. dos; SILVA JÚNIOR, A. C. da; CRUZ, C. D.; NASCIMENTO, M.; FERREIRA, R. de P.; VILELA, D. Potential of dry matter yield from alfalfa germplasm in composing base populations. Crop Breeding and Applied Biotechnology, v. 21, n. 2, e36702132, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 9. | | SILVA JÚNIOR, A. C. da; PONTES, D.; ROSADO, R. D. S.; VILELA, D.; FERREIRA, R. de P.; NASCIMENTO, M.; BHERING, L.; CRUZ, C. D. Multi-information analysis to recommend alfalfa cultivars for adaptability and phenotypic stability. Crop Breeding and Applied Biotechnology, v. 25, n. 2, e45012525, 2025.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 4 |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 10. | | OLIVEIRA, C. F. de; SOUZA, J. E. de; SIQUEIRA, M. J. da S.; SILVA JÚNIOR, A. C. da; FERREIRA, R. de P.; VILELA, D.; CRUZ, C. D. Selection of alfalfa genotypes for dry matter yield and persistence with repeated measures. Agronomy Science and Biotechnology, v. 9, p. 1-14, 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 4 |
| Biblioteca(s): Embrapa Gado de Leite; Embrapa Pecuária Sudeste. |
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| 12. | | SILVA, C. M. da; VASCONCELOS, S. S.; MOURÃO JUNIOR, M.; BISPO, C. J. C.; KATO, O. R.; SILVA JUNIOR, A. C. da; CASTELLANI, D. C. Variação temporal do efluxo de CO2 do solo em sistemas agroflorestais com palma de óleo na Amazônia Oriental. Acta Amazonica, Manaus, v. 46, n. 1, p. 1-12, jan./mar. 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| 13. | | SILVA JÚNIOR, A. C. da; COSTA, W. G. da; GUIMARÃES, A. G.; MOURA, W. de M.; CAMPOS, L. J. M.; RODRIGUES, R. de C.; BHERING, L. L.; CRUZ, C. D.; EVARISTO, A. B. Bayesian inference applied to soybean grown under different shading levels using the multiple-trait model. Scientia Agricola, v. 81, e20220233, 2024. 7 p.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Soja. |
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| 14. | | SILVA JUNIOR, A. C. da; SOUZA, P. J. de O. P. de; SOUSA, D. de P.; MARTORANO, L. G.; SILVA, C. M. da; SILVA, C. M. da; NUNES, H. G. G. C.; LIMA, M. J. A. de; SOUSA, A. M. L. de; PINTO, J. V. de N.; RUIVO, M. de L. P.; ALVES, J. D. N.; CONCEIÇÃO, H. E. O. da. Energy balance, water demand, and crop coefficient of acid lime in the Oriental Amazon. Water, v. 15, n. 6, Article 1239, 2023.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 3 |
| Biblioteca(s): Embrapa Amazônia Oriental. |
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| Registros recuperados : 14 | |
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