KeSACNN: a protein-protein interaction article classification approach based on deep neural network
by Ling Luo; Zhihao Yang; Lei Wang; Yin Zhang; Hongfei Lin; Jian Wang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 22, No. 2, 2019

Abstract: Automatic classification of protein-protein interaction (PPI) relevant articles from biomedical literature is a crucial step for biological database curation since it can help reduce the curation burden at the initial stage. However, most popular PPI article classification methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Recent years, PPI article classification with neural networks has gained increasing attention, but domain knowledge has been rarely used in these methods. Aiming to exploit domain knowledge, we propose a domain Knowledge-enriched Self-Attention Convolutional Neural Network (KeSACNN) approach for PPI article classification. In this approach, two knowledge embeddings are proposed, and the novel convolution neural network architectures with self-attention mechanism are designed to leverage biomedical knowledge. The experimental results show that our method achieves the state-of-the-art performance on the BioCreative II and III corpora (82.92% and 67.93% in F-scores, respectively).

Online publication date: Mon, 20-May-2019

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