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Descripción
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| Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein- protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non- machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Ieee-Acm Transactions on Computational Biology And Bioinformatics |
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ISSN
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1545-5963 |
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Factor de impacto JCR
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1,955 |
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Información de impacto
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Volumen
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DOI
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Número de revista
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Desde la página
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687 |
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Hasta la página
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695 |
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Mes
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SIN MES |
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Ranking
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