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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
08/08/2002 |
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Data da última atualização: |
30/01/2008 |
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Autoria: |
LIN, S. M.; JOHNSON, K. F. (ed.). |
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Afiliação: |
Duke University Medical Center. |
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Título: |
Methods of microarray data analysis: papers from CAMDA '00. |
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Ano de publicação: |
2002 |
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Fonte/Imprenta: |
Boston: Kluwer, 2002. |
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Páginas: |
189 p. |
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ISBN: |
0-7923-7564-5 |
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Idioma: |
Inglês |
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Notas: |
Conference on the Critical Assessment of microarray Data Analysis (CAMDA), 2000. |
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Conteúdo: |
Data mining and machine learning methods for microarray analysis. (Werner Dubitzky, Martin Granzow, Daniel Berrar). Evolutionary computation in microarray data analysis. ( Jason H. Moore and Joel S. Parker). Using non-parametric methods in the context of multiple testing to determine differentially expressed genes. (Gregory Grant, Elisabetta Manduchi, Christian Stoeckert, Jr.). Iterative linear regression by sector. (David B. Finkelstein, Rob Ewing, Jeremy Gollub, Frederik Sterky, Shauna Somerville, and J. Michael Cherry). A method to improve detection of disease using selectively expressed genes in microarray data. (Virgine Aris and Michael Recce). Computational analysis of leukemia microarray expression data using the GA-KNN method. (Leping Li, Lee. G. Pedersen, Thomas A. Darden and Clarice R. Weinberg). Classical statistical approaches to molecular classification of cancer from gene expression profiling. (Jun Lu, Sarah Hardy, Wen-Li Tao, Spencer Muse, Bruce Weir and Susan Spruill). Classification of acute leukemia based on DNA microarray gene expressions using partial least squares. (Danh V. Nguyen and David M. Rocke). Applying classification separability analysis to microarray data. (Zhen Zhang, Grier Page, and Hong Zhang). How many genes are needed for a discriminant microarray data analysis. (Wentian Li and Yaning Yang). Comparing symbolic and subsymbolic macjine learning approaches to classification of cancer and gene identification. (Werner Dubitzky, Martin Granzow, Daniel Berrar). Applying machine learning techniques to analysis of gene expression data: cancer diagnosis. (Kyu-Baek Hwang, Dong-Yeon Cho, Sang-Wook Park, Sung-Dong Kim, and Byoung-Tak Zhang). MenosData mining and machine learning methods for microarray analysis. (Werner Dubitzky, Martin Granzow, Daniel Berrar). Evolutionary computation in microarray data analysis. ( Jason H. Moore and Joel S. Parker). Using non-parametric methods in the context of multiple testing to determine differentially expressed genes. (Gregory Grant, Elisabetta Manduchi, Christian Stoeckert, Jr.). Iterative linear regression by sector. (David B. Finkelstein, Rob Ewing, Jeremy Gollub, Frederik Sterky, Shauna Somerville, and J. Michael Cherry). A method to improve detection of disease using selectively expressed genes in microarray data. (Virgine Aris and Michael Recce). Computational analysis of leukemia microarray expression data using the GA-KNN method. (Leping Li, Lee. G. Pedersen, Thomas A. Darden and Clarice R. Weinberg). Classical statistical approaches to molecular classification of cancer from gene expression profiling. (Jun Lu, Sarah Hardy, Wen-Li Tao, Spencer Muse, Bruce Weir and Susan Spruill). Classification of acute leukemia based on DNA microarray gene expressions using partial least squares. (Danh V. Nguyen and David M. Rocke). Applying classification separability analysis to microarray data. (Zhen Zhang, Grier Page, and Hong Zhang). How many genes are needed for a discriminant microarray data analysis. (Wentian Li and Yaning Yang). Comparing symbolic and subsymbolic macjine learning approaches to classification of cancer and gene identification. (Werner Dubitzky, Martin Granzow, ... Mostrar Tudo |
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Palavras-Chave: |
Expressão de genes. |
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Thesagro: |
Análise de Dados. |
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Thesaurus Nal: |
DNA microarrays. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02254nam a2200193 a 4500
001 1004946
005 2008-01-30
008 2002 bl uuuu 00u1 u #d
020 $a0-7923-7564-5
100 1 $aLIN, S. M.
245 $aMethods of microarray data analysis$bpapers from CAMDA '00.
260 $aBoston: Kluwer$c2002
300 $a189 p.
500 $aConference on the Critical Assessment of microarray Data Analysis (CAMDA), 2000.
520 $aData mining and machine learning methods for microarray analysis. (Werner Dubitzky, Martin Granzow, Daniel Berrar). Evolutionary computation in microarray data analysis. ( Jason H. Moore and Joel S. Parker). Using non-parametric methods in the context of multiple testing to determine differentially expressed genes. (Gregory Grant, Elisabetta Manduchi, Christian Stoeckert, Jr.). Iterative linear regression by sector. (David B. Finkelstein, Rob Ewing, Jeremy Gollub, Frederik Sterky, Shauna Somerville, and J. Michael Cherry). A method to improve detection of disease using selectively expressed genes in microarray data. (Virgine Aris and Michael Recce). Computational analysis of leukemia microarray expression data using the GA-KNN method. (Leping Li, Lee. G. Pedersen, Thomas A. Darden and Clarice R. Weinberg). Classical statistical approaches to molecular classification of cancer from gene expression profiling. (Jun Lu, Sarah Hardy, Wen-Li Tao, Spencer Muse, Bruce Weir and Susan Spruill). Classification of acute leukemia based on DNA microarray gene expressions using partial least squares. (Danh V. Nguyen and David M. Rocke). Applying classification separability analysis to microarray data. (Zhen Zhang, Grier Page, and Hong Zhang). How many genes are needed for a discriminant microarray data analysis. (Wentian Li and Yaning Yang). Comparing symbolic and subsymbolic macjine learning approaches to classification of cancer and gene identification. (Werner Dubitzky, Martin Granzow, Daniel Berrar). Applying machine learning techniques to analysis of gene expression data: cancer diagnosis. (Kyu-Baek Hwang, Dong-Yeon Cho, Sang-Wook Park, Sung-Dong Kim, and Byoung-Tak Zhang).
650 $aDNA microarrays
650 $aAnálise de Dados
653 $aExpressão de genes
700 1 $aJOHNSON, K. F.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
31/01/2018 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
STAFUZZA, N. B.; ZERLOTINI NETO, A.; LOBO, F. P.; YAMAGISHI, M. E. B.; CHUD, T. C. S.; CAETANO, A. R.; MUNARI, D. P.; GARRICK, D. J.; COLE, J. B.; MACHADO, M. A.; MARTINS, M. F.; CARVALHO, M. R.; SILVA, M. V. G. B. |
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Afiliação: |
FCAV/Unesp; ADHEMAR ZERLOTINI NETO, CNPTIA; FRANCISCO PEREIRA LOBO, CNPTIA; MICHEL EDUARDO BELEZA YAMAGISHI, CNPTIA; FCAV/Unesp; ALEXANDRE RODRIGUES CAETANO, Cenargen; FCAV/Unesp; Iowa State University; Agricultural Research Service; MARCO ANTONIO MACHADO, CNPGL; MARTA FONSECA MARTINS, CNPGL; UFMG; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL. |
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Título: |
Single nucleotide variants and indels identified from whole-genome resequencing of Gyr, Girolando, and Holstein cattle breeds. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Journal of Animal Science, v. 95, p. 80-81, 2017. |
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DOI: |
10.2527/asasann.2017.164 |
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Idioma: |
Inglês |
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Notas: |
Suplemento 4, Resumo 164. Edição de Abstracts do ASAS-CSAS Annual Meeting and Trade Show, Baltimore, 2017. Na publicação: A. Zerlotini, M. V. G. B. da Silva. |
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Conteúdo: |
Whole-genome resequencing, alignment, and annotation analyses were undertaken for ten sires representing Gyr, Girolando, and Holstein cattle breeds to detect and make publicly available genome-wide single nucleotide variations (SNVs) and insertions/deletions (InDels). |
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Palavras-Chave: |
Composite breed; Indels; Raças de gado; Single nucleotide variants. |
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Thesagro: |
Gado; Variação genética. |
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Thesaurus NAL: |
Cattle breeds; genome. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01513nam a2200373 a 4500
001 2086834
005 2020-01-07
008 2017 bl uuuu u00u1 u #d
024 7 $a10.2527/asasann.2017.164$2DOI
100 1 $aSTAFUZZA, N. B.
245 $aSingle nucleotide variants and indels identified from whole-genome resequencing of Gyr, Girolando, and Holstein cattle breeds.$h[electronic resource]
260 $aJournal of Animal Science, v. 95, p. 80-81$c2017
500 $aSuplemento 4, Resumo 164. Edição de Abstracts do ASAS-CSAS Annual Meeting and Trade Show, Baltimore, 2017. Na publicação: A. Zerlotini, M. V. G. B. da Silva.
520 $aWhole-genome resequencing, alignment, and annotation analyses were undertaken for ten sires representing Gyr, Girolando, and Holstein cattle breeds to detect and make publicly available genome-wide single nucleotide variations (SNVs) and insertions/deletions (InDels).
650 $aCattle breeds
650 $agenome
650 $aGado
650 $aVariação genética
653 $aComposite breed
653 $aIndels
653 $aRaças de gado
653 $aSingle nucleotide variants
700 1 $aZERLOTINI NETO, A.
700 1 $aLOBO, F. P.
700 1 $aYAMAGISHI, M. E. B.
700 1 $aCHUD, T. C. S.
700 1 $aCAETANO, A. R.
700 1 $aMUNARI, D. P.
700 1 $aGARRICK, D. J.
700 1 $aCOLE, J. B.
700 1 $aMACHADO, M. A.
700 1 $aMARTINS, M. F.
700 1 $aCARVALHO, M. R.
700 1 $aSILVA, M. V. G. B.
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