Transfer learning for the prediction of human-virus protein-protein interaction

Allowing the prediction of human-virus protein-protein interactions (PPI), our algorithm is based on a Siamese Convolutional Neural Network architecture (CNN), accounting for pre-acquired protein evolutionary profiles (i.e. PSSM) as input. In combinations with a multilayer perceptron, we evaluate our model on a variety of human-virus PPI datasets and compare its results with traditional machine learning frameworks, a deep learning architecture and several other human-virus PPI prediction methods, showing superior performance. Furthermore, we propose two transfer learning methods, allowing the reliable prediction of interactions in cross-viral settings, where we train our system with PPIs in a source human-virus domain and predict interactions in a target human-virus domain. Notable, we observed that our transfer learning approaches allowed the reliable prediction of PPIs in relatively less investigated human-virus domains, such as Dengue, Zika and SARS-CoV-2.