We propose a semi-supervised learning framework which utilizes representation learning, positive-unlabeled (PU) learning and meta-learning efficiently to predict the drug interactions. Information from multiple data sources is used to create feature networks, which is used to learn the meta-knowledge about the DDIs. Given that DDIs have only positive labeled data, a PU learning-based classifier is used to generate meta-knowledge from feature networks. Finally, a meta-classifier that combines the predicted probability of interaction from the meta-knowledge learnt is designed.
Node2vec, a network representation learning method and bagging SVM, a PU learning algorithm, are used in this work. Both representation learning and PU learning algorithms improve the performance of the system by 22% and 12.7% respectively. The meta-classifier performs better and predicts more reliable DDIs than the base classifiers.