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Memorias de investigación
Communications at congresses:
Iteratively collective prediction of disease-gene associations through the incomplete network
Year:2017
Research Areas
  • Computational biology
Information
Abstract
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 ?
International
Si
Congress
International Conference on Bioinformatics and Biomedicine (BIBM) 2017
960
Place
Reviewers
Si
ISBN/ISSN
978-1-5090-3050-7
Start Date
13/11/2017
End Date
16/11/2017
From page
1324
To page
1330
IEEE International Conference on Bioinformatics and Biomedicine
Participants
  • Autor: Xiangyi Meng
  • Autor: Quan Zou
  • Autor: Alfonso Vicente Rodriguez-Paton Aradas (UPM)
  • Autor: Xiangxiang Zeng (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Inteligencia Artificial (LIA)
  • Departamento: Inteligencia Artificial
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