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Registro Completo |
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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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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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Registro original: |
Embrapa Soja (CNPSO) |
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Registro Completo
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Biblioteca(s): |
Embrapa Semiárido. |
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Data corrente: |
01/04/2021 |
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Data da última atualização: |
06/09/2022 |
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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: |
GAVA, C. A. T.; GIONGO, V.; SIGNOR, D.; FERNANDES JUNIOR, P. I. |
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Afiliação: |
CARLOS ALBERTO TUAO GAVA, CPATSA; VANDERLISE GIONGO, CPATSA; DIANA SIGNOR DEON, CPATSA; PAULO IVAN FERNANDES JUNIOR, CPATSA. |
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Título: |
Land-use change alters the stocks of carbon, nitrogen, and phosphorus in a Haplic Cambisol in the Brazilian semi-arid region. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Soil Use and Management, v. 38, n. 1, p. 953-963, 2022. |
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DOI: |
10.1111/sum.12716 |
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Idioma: |
Inglês |
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Conteúdo: |
Land- use change (LUC) can impact soil quality. In semi- arid areas of Brazil, impacts of LUC need to be understood for better soil management. This study evaluated the impact of LUC on soil organic carbon (SOC), nitrogen (N), and phosphorus (P) dis-tributions through the soil profile and stocks of a Haplic- Cambisol in the Brazilian Semi- arid region. Three land- use systems (LUS) were investigated: agricultural man-agement (30years), regeneration under controlled grazing (25years) after 5years arable management, and native dry forest. Soil contents of P, total C, total N, and N fractions were used to calculate stocks and their stoichiometric ratios for layers 0? 5, 5? 10, 10? 20, and 20? 40cm. Data from these LUS were compared using Kruskal? Wallis non- parametric tests. Changes to soil microbial biomass reflected the sub-stantially reduced SOC concentration and stock in managed soils compared with that of the natural dry forest area. Total N stock was not affected by LUC, although increases in nitrate and ammonium offset a significantly reduced organic N fraction in the agricultural area. The largest P stock was found in agricultural land, followed by the grazed fallow regeneration site. LUC significantly influenced the stoichiomet-ric ratio of C, N, and P, with the change from Caatinga to agriculture affecting the equilibrium between organic residues? input and mineralization. LUC resulted in sig-nificant changes to C, N, and P stocks, which did not recover to the original values, even after 25years of regeneration under controlled grazing. MenosLand- use change (LUC) can impact soil quality. In semi- arid areas of Brazil, impacts of LUC need to be understood for better soil management. This study evaluated the impact of LUC on soil organic carbon (SOC), nitrogen (N), and phosphorus (P) dis-tributions through the soil profile and stocks of a Haplic- Cambisol in the Brazilian Semi- arid region. Three land- use systems (LUS) were investigated: agricultural man-agement (30years), regeneration under controlled grazing (25years) after 5years arable management, and native dry forest. Soil contents of P, total C, total N, and N fractions were used to calculate stocks and their stoichiometric ratios for layers 0? 5, 5? 10, 10? 20, and 20? 40cm. Data from these LUS were compared using Kruskal? Wallis non- parametric tests. Changes to soil microbial biomass reflected the sub-stantially reduced SOC concentration and stock in managed soils compared with that of the natural dry forest area. Total N stock was not affected by LUC, although increases in nitrate and ammonium offset a significantly reduced organic N fraction in the agricultural area. The largest P stock was found in agricultural land, followed by the grazed fallow regeneration site. LUC significantly influenced the stoichiomet-ric ratio of C, N, and P, with the change from Caatinga to agriculture affecting the equilibrium between organic residues? input and mineralization. LUC resulted in sig-nificant changes to C, N, and P stocks, whi... Mostrar Tudo |
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Palavras-Chave: |
Cambissolo Haplic; Carbono do solo; Fósforo do solo; Nitrogênio do solo; Semiárido. |
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Thesagro: |
Dióxido de Carbono; Fixação de Fósforo; Fixação de Nitrogênio; Fósforo; Nitrogênio; Solo; Uso da Terra. |
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Thesaurus NAL: |
Land use; Land use planning; Soil. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1131020/1/Land-use-change-alters-the-stocks-of-carbon-2022.pdf
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Marc: |
LEADER 02631naa a2200349 a 4500
001 2131020
005 2022-09-06
008 2022 bl uuuu u00u1 u #d
024 7 $a10.1111/sum.12716$2DOI
100 1 $aGAVA, C. A. T.
245 $aLand-use change alters the stocks of carbon, nitrogen, and phosphorus in a Haplic Cambisol in the Brazilian semi-arid region.$h[electronic resource]
260 $c2022
520 $aLand- use change (LUC) can impact soil quality. In semi- arid areas of Brazil, impacts of LUC need to be understood for better soil management. This study evaluated the impact of LUC on soil organic carbon (SOC), nitrogen (N), and phosphorus (P) dis-tributions through the soil profile and stocks of a Haplic- Cambisol in the Brazilian Semi- arid region. Three land- use systems (LUS) were investigated: agricultural man-agement (30years), regeneration under controlled grazing (25years) after 5years arable management, and native dry forest. Soil contents of P, total C, total N, and N fractions were used to calculate stocks and their stoichiometric ratios for layers 0? 5, 5? 10, 10? 20, and 20? 40cm. Data from these LUS were compared using Kruskal? Wallis non- parametric tests. Changes to soil microbial biomass reflected the sub-stantially reduced SOC concentration and stock in managed soils compared with that of the natural dry forest area. Total N stock was not affected by LUC, although increases in nitrate and ammonium offset a significantly reduced organic N fraction in the agricultural area. The largest P stock was found in agricultural land, followed by the grazed fallow regeneration site. LUC significantly influenced the stoichiomet-ric ratio of C, N, and P, with the change from Caatinga to agriculture affecting the equilibrium between organic residues? input and mineralization. LUC resulted in sig-nificant changes to C, N, and P stocks, which did not recover to the original values, even after 25years of regeneration under controlled grazing.
650 $aLand use
650 $aLand use planning
650 $aSoil
650 $aDióxido de Carbono
650 $aFixação de Fósforo
650 $aFixação de Nitrogênio
650 $aFósforo
650 $aNitrogênio
650 $aSolo
650 $aUso da Terra
653 $aCambissolo Haplic
653 $aCarbono do solo
653 $aFósforo do solo
653 $aNitrogênio do solo
653 $aSemiárido
700 1 $aGIONGO, V.
700 1 $aSIGNOR, D.
700 1 $aFERNANDES JUNIOR, P. I.
773 $tSoil Use and Management$gv. 38, n. 1, p. 953-963, 2022.
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