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bioRxiv · 10.1101/499244

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

Wan, C.·Cozzetto, D.·Fa, R.·Jones, D. T.
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Abstract

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

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Wan, C., Cozzetto, D., Fa, R., Jones, D. T.. 2018-12-17. Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks. https://doi.org/10.1101/499244

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