Deep recurrent neural networks in HIV-1 protease cleavage classification

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Abstract

Many of the drugs that are used by HIV infected humans can be catalogued as protease inhibitors. These drugs mainly restrict the protease activity and therefore reduce the formation of mature proteins. By studying the protease and predicting its cleavage sites, one can hope to achieve better drugs. Predicting HIV-1 protease cleavage problem has been addressed by many machine learning approaches but its classification false positive and the lack of an approach with good generalisation remains a challenge. Furthermore, deep neural networks have shown many promising results in machine learning community. Therefore, we present a new deep learning approach to predict protease cleavage sites based on bidirectional Gated Recurrent Units and feed-forward networks. We have tested this approach on several datasets and achieved 89.04% accuracy and 92.45% AUC on average.

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