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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