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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
06/08/2009 |
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Data da última atualização: |
10/11/2010 |
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Autoria: |
ALMEIDA, I. L.; PECHMANN, D. R.; AMARANTE, M. de B. do; CECHIN, A. L. |
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Afiliação: |
IGOR LORENZATO ALMEIDA, Instituto Federal Farroupilha; DENISE REGINA PECHMANN, Instituto Federal Farroupilha; MAICON DE BRITO DO AMARANTE, UFRGS; ADELMO LUIS CECHIN, UNISINOS. |
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Título: |
Gene expression analysis using markov chains extracted from recurrent neural networks. |
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Ano de publicação: |
2009 |
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Fonte/Imprenta: |
In: CONGRESSO DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO, 29., 2009, Bento Gonçalves. Anais... Rio Grande do SUL: Instituto de Informática UFRGS. |
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Páginas: |
627-636. |
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Idioma: |
Inglês |
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Notas: |
CSBC 2009. |
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Conteúdo: |
This paper presents a new approach for the analysis of microarray data by the use of Recurrent Neural Networks (RNNs) as a time model of the gene regulatory network. Our method extracts a Markov Chain (MC) from a trained RNN and the relations among genes in each MC state. We propose to use the learning ability of RNNs for the automatic construction of the model with the gene interactions represented by the weights and afterwards to use an algorithm to extract these relations in the form of MCs and linear matrices easily visualized in the form of graphs of states and genes. The graph of states show the evolution of the gene expression levels in time while the gene graph shows the dependencies among genes in each Markov state. |
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Palavras-Chave: |
Análise de expressão genética; Markov Chain (MC); Modelo temporal; Processamento de dados; Recurrent Neural Networks (RNNs); Redes neurais. |
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Thesagro: |
Genética. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01600naa a2200265 a 4500
001 1256498
005 2010-11-10
008 2009 bl uuuu u00u1 u #d
100 1 $aALMEIDA, I. L.
245 $aGene expression analysis using markov chains extracted from recurrent neural networks.
260 $c2009
300 $a627-636.
500 $aCSBC 2009.
520 $aThis paper presents a new approach for the analysis of microarray data by the use of Recurrent Neural Networks (RNNs) as a time model of the gene regulatory network. Our method extracts a Markov Chain (MC) from a trained RNN and the relations among genes in each MC state. We propose to use the learning ability of RNNs for the automatic construction of the model with the gene interactions represented by the weights and afterwards to use an algorithm to extract these relations in the form of MCs and linear matrices easily visualized in the form of graphs of states and genes. The graph of states show the evolution of the gene expression levels in time while the gene graph shows the dependencies among genes in each Markov state.
650 $aGenética
653 $aAnálise de expressão genética
653 $aMarkov Chain (MC)
653 $aModelo temporal
653 $aProcessamento de dados
653 $aRecurrent Neural Networks (RNNs)
653 $aRedes neurais
700 1 $aPECHMANN, D. R.
700 1 $aAMARANTE, M. de B. do
700 1 $aCECHIN, A. L.
773 $tIn: CONGRESSO DA SOCIEDADE BRASILEIRA DE COMPUTAÇÃO, 29., 2009, Bento Gonçalves. Anais... Rio Grande do SUL: Instituto de Informática UFRGS.
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Embrapa Agricultura Digital (CNPTIA) |
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