Reference : Detecting disease genes based on semi-supervised learning and protein--protein intera...
Scientific journals : Article
Human health sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/21697
Detecting disease genes based on semi-supervised learning and protein--protein interaction networks
English
Nguyen, Thanh Phuong mailto [The Microsoft Research, University of Trento Centre for Computational Systems Biology (COSBI)]
Ho, Tu-Bao [Japan Advance Institute of Science and Technology]
2012
Artificial Intelligence in Medicine
Elsevier Science
54
1
63--71
Yes (verified by ORBilu)
International
0933-3657
Amsterdam
The Netherlands
[en] Objective

Predicting or prioritizing the human genes that cause disease, or “disease genes”, is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is carried out upon the key assumption of “the network-neighbour of a disease gene is likely to cause the same or a similar disease”, and mostly employs data regarding well-known disease genes, using supervised learning methods. This work aims to find an effective method to exploit the disease gene neighbourhood and the integration of several useful omics data sources, which potentially enhance disease gene predictions.

Methods

We have presented a novel method to effectively predict disease genes by exploiting, in the semi-supervised learning (SSL) scheme, data regarding both disease genes and disease gene neighbours via protein–protein interaction network. Multiple proteomic and genomic data were integrated from six biological databases, including Universal Protein Resource, Interologous Interaction Database, Reactome, Gene Ontology, Pfam, and InterDom, and a gene expression dataset.

Results

By employing a 10 times stratified 10-fold cross validation, the SSL method performs better than the k-nearest neighbour method and the support vector machines method in terms of sensitivity of 85%, specificity of 79%, precision of 81%, accuracy of 82%, and a balanced F-function of 83%. The other comparative experimental evaluations demonstrate advantages of the proposed method given a small amount of labeled data with accuracy of 78%. We have applied the proposed method to detect 572 putative disease genes, which are biologically validated by some indirect ways.

Conclusion

Semi-supervised learning improved ability to study disease genes, especially a specific disease when the known disease genes (as labeled data) are very often limited. In addition to the computational improvement, the analysis of predicted disease proteins indicates that the findings are beneficial in deciphering the pathogenic mechanisms.
http://hdl.handle.net/10993/21697

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