TY - JOUR
T1 - Integrating multiple networks for protein function prediction
AU - Yu, Guoxian
AU - ZHU, Hailong
AU - Domeniconi, Carlotta
AU - Guo, Maozu
N1 - Funding Information:
We are grateful to the authors of the competitive algorithms for providing their code for the experimental study. This work is partially supported by the Research Grants Council of Hong Kong (No. 212111 and 212613), Natural Science Foundation of China (No. 61271346 and 61402378), Natural Science Foundation of CQ CSTC (No. cstc2014jcyjA40031), Fundamental Research Funds for the Central Universities of China (No. XDJK2014C044) and Doctoral Fund of Southwest University (No. SWU113034).
Funding Information:
Acknowledgements We are grateful to the authors of the competitive algorithms for providing their code for the experimental study. This work is partially supported by the Research Grants Council of Hong Kong (No. 212111 and 212613), Natural Science Foundation of China (No. 61271346 and 61402378), Natural Science Foundation of CQ CSTC (No. cstc2014jcyjA40031), Fundamental Research Funds for the Central Universities of China (No. XDJK2014C044) and Doctoral Fund of Southwest University (No. SWU113034).
PY - 2015/1/21
Y1 - 2015/1/21
N2 - Background: High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. Results: We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms. Conclusion: MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request.
AB - Background: High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. Results: We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms. Conclusion: MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request.
UR - http://www.scopus.com/inward/record.url?scp=84928711013&partnerID=8YFLogxK
U2 - 10.1186/1752-0509-9-S1-S3
DO - 10.1186/1752-0509-9-S1-S3
M3 - Article
C2 - 25707434
AN - SCOPUS:84928711013
VL - 9
JO - BMC Systems Biology
JF - BMC Systems Biology
SN - 1752-0509
M1 - S3
ER -