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Descripción
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| The prediction of links between genes and disease is still one of the biggest challenges in the field of human health. Almost all state-of-the-art studies on the prediction of gene- disease links focuson a single pair of links, ignoring the associations and interactions among different types of links. Moreover, the biological information networks are usually incomplete. In this paper, we study the similarity measure to be used on two different types of nodes, based on the metapaths between them (Wsrm). Then an iterative self-updating approach for link prediction using heterogeneous information network is proposed to fit the incompletion of the network (ISL), which is a semi-supervised learning formula. Using the biological integrated network constructed from OMIM and HumanNet dataset (30,896 nodes and 1,200,166 edges) we applied our framework. The area under the receiver operating ? | |
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Internacional
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Si |
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Nombre congreso
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International Conference on Bioinformatics and Biomedicine (BIBM) 2017 |
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Tipo de participación
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960 |
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Lugar del congreso
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Revisores
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Si |
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ISBN o ISSN
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978-1-5090-3050-7 |
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DOI
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Fecha inicio congreso
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13/11/2017 |
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Fecha fin congreso
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16/11/2017 |
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Desde la página
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1324 |
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Hasta la página
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1330 |
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Título de las actas
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IEEE International Conference on Bioinformatics and Biomedicine |