Graph embedding and ensemble learning for predicting gene-disease associations
by Haorui Wang; Xiaochan Wang; Zhouxin Yu; Wen Zhang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 23, No. 4, 2020

Abstract: The discovery of gene-disease associations is important for preventing, diagnosing, and treating diseases. In this paper, we propose two heterogeneous network-based methods that enhance gene-disease association prediction by using graph embedding and ensemble learning, abbreviated as 'HNEEM' and 'HNEEM-PLUS'. We integrate gene-disease associations, gene-chemical associations, gene-gene associations and disease-chemical associations to construct a heterogeneous network, and adopt six graph embedding methods respectively to learn the representative vectors of genes and diseases from the network. We build individual prediction models using each graph embedding representation and random forest, and then combine them by average scoring to construct the ensemble model HNEEM. To increase the diversity of base predictors, we further introduce the multilayer perceptron as an additional classifier and generate more base predictors, and thus propose an extended method named 'HNEEM-PLUS'. Computational experiments show that HNEEM has better results than individual methods and HNEEM-PLUS makes more improvement than HNEEM.

Online publication date: Mon, 27-Jul-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com