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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
28/01/2013 |
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Data da última atualização: |
28/01/2013 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
ZANELLA, L.; BORÉM, R. A. T.; SOUZA, C. G. S.; BORÉM, F. M; ALVES, H. M. R. |
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Afiliação: |
LISIANE ZANELLA, UFLA; ROSÂNGELA A. T. BORÉM, UFLA; CAROLINA G. S. SOUZA, UFLA; FLÁVIO M. BORÉM, UFLA; HELENA MARIA RAMOS ALVES, SAPC. |
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Título: |
Relationship between coffee and environmental preservation in the Serra da Mantiqueira, Minas Gerais, Brazil. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE ON COFFEE SCIENCE, 2012, San José. Proceedings... [S.l]: Association for Science and Information on Coffee (ASIC), 2012. p. 223. |
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Páginas: |
p. 280 |
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Idioma: |
Inglês |
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Conteúdo: |
The human activities have caused loss and fragmentation of natural habitats, and seriously threatened the conservation of biodiversity. Geographic Information Systems and Remote Sensing enables us to analyze the physical structure of different landscapes, and helps in deciding strategies for conservation. In this study, geotechnologies were used to carry out the environmental characterization of the Serra da Mantiqueira region of Minas Gerais, and create a geographic database. |
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Palavras-Chave: |
Coffee. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01150nam a2200181 a 4500
001 1946536
005 2013-01-28
008 2012 bl uuuu u00u1 u #d
100 1 $aZANELLA, L.
245 $aRelationship between coffee and environmental preservation in the Serra da Mantiqueira, Minas Gerais, Brazil.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE ON COFFEE SCIENCE, 2012, San José. Proceedings... [S.l]: Association for Science and Information on Coffee (ASIC), 2012. p. 223.$c2012
300 $ap. 280
520 $aThe human activities have caused loss and fragmentation of natural habitats, and seriously threatened the conservation of biodiversity. Geographic Information Systems and Remote Sensing enables us to analyze the physical structure of different landscapes, and helps in deciding strategies for conservation. In this study, geotechnologies were used to carry out the environmental characterization of the Serra da Mantiqueira region of Minas Gerais, and create a geographic database.
653 $aCoffee
700 1 $aBORÉM, R. A. T.
700 1 $aSOUZA, C. G. S.
700 1 $aBORÉM, F. M
700 1 $aALVES, H. M. R.
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Registro original: |
Embrapa Café (CNPCa) |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
30/05/2016 |
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Data da última atualização: |
26/07/2017 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
LOPES, I. de O. N.; SCHLIEP, A.; CARVALHO, A. P. de L. F. de. |
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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. |
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Título: |
Automatic learning of pre-miRNAs from different species. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
BMC Bioinformatics, v. 17, n. 224, 18 p., 2016. |
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ISSN: |
1471-2105 |
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DOI: |
10.1186/s12859-016-1036-3 |
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Idioma: |
Português |
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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 |
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Palavras-Chave: |
Bioinformática. |
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Thesagro: |
Automação; Biologia. |
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Thesaurus NAL: |
Bioinformatics; Biological Sciences. |
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Categoria do assunto: |
S Ciências Biológicas |
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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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