WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network
Abstract Background An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disea...
Main Authors: | Yahui Long, Jiawei Luo |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2019-11-01
|
Series: | BMC Bioinformatics |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s12859-019-3066-0 |
Similar Items
-
WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks
by: Jinli Zhang, et al.
Published: (2020-01-01) -
Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization
by: Bin-Sheng He, et al.
Published: (2018-11-01) -
A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks
by: Jinli Zhang, et al.
Published: (2020-02-01) -
A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network
by: Hao Li, et al.
Published: (2019-04-01) -
Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network
by: Xiujuan Lei, et al.
Published: (2020-04-01)