These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts.
Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases.
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