Predicting protein functions by using non-negative matrix factorisation with multi-networks co-regularisation
by Wei Peng; Jielin Du; Lun Li; Wei Dai; Wei Lan
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 23, No. 4, 2020

Abstract: It is a hot research field to design an effective method for protein function prediction by integrating heterogeneous biological data. In this work, we proposed a novel non-Negative Matrix Factorisation-based method, namely PONMF-S to learn protein and GO features from different biological networks for protein function prediction. Additionally, we extend PONMF-S to other versions by considering the function influence of proteins' neighbours and GO terms' neighbours. We apply our methods and two state-of-the-art methods (UBiRW and NMFGO) to predict functions for proteins of Saccharomyces cerevisiae and Homo sapiens. The prediction results show that PONMF-S outperforms the other two existing methods when randomly removing a part of known function information. When predicting functions for the proteins that have not any known ahead functional information, PONMF-S improves the prediction performance of NMFGO significantly and is comparable with UBiRW. Besides, the extended version of PONMF-S can even outperform UBiRW in most function categories.

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