Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Iteratively collective prediction of disease-gene associations through the incomplete network
Año:2017
Áreas de investigación
  • Bioinformática
Datos
Descripción
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 ?
Internacional
Si
Nombre congreso
International Conference on Bioinformatics and Biomedicine (BIBM) 2017
Tipo de participación
960
Lugar del congreso
Revisores
Si
ISBN o ISSN
978-1-5090-3050-7
DOI
Fecha inicio congreso
13/11/2017
Fecha fin congreso
16/11/2017
Desde la página
1324
Hasta la página
1330
Título de las actas
IEEE International Conference on Bioinformatics and Biomedicine
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Xiangyi Meng
  • Autor: Quan Zou
  • Autor: Alfonso Vicente Rodriguez-Paton Aradas (UPM)
  • Autor: Xiangxiang Zeng (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Inteligencia Artificial (LIA)
  • Departamento: Inteligencia Artificial
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