Predicting the interactions between plant lncRNA-encoded peptide and protein using domain knowledge-based prototypical network

Long noncoding RNA(lncRNA) has been reported to encode small peptides which play key roles in life activities through their functions by binding to proteins. It is crucial to predict the interactions between the lncRNA-encoded peptide and protein. However, no computational methods have been designed for predicting this type of interactions directly, owing to the few-shot problem causing poor generalization. Prototypical network (ProtoNet) is a classic learner for few-shot learning. However, how to obtain effective embedding and measure the distance between different prototypes accurately are the most important challenges. Although some improved prototypical networks have been proposed, they ignore the role of domain knowledge which is helpful for constructing models conforming to the domain mechanism In this study, we propose a novel method for interactions prediction between plant lncRNA-encoded peptide and protein using domain knowledge-based ProtoNet (IPLncPP-DKPN). Multiple features that imply domain knowledge are extracted, connected, and converted to avoid sparse and enhance information using a dual-routing parallel feature dimensionality reduction algorithm IProtoNet is an improved ProtoNet using capsule network-based embedding and Mahalanobis distance-based prototype. The converted features are fed into IProtoNet to realize the classification task. The experimental results manifest that IPLncPP-DKPN achieves better performance on the independent test set compared with classic machine learning models. To the best of our knowledge, IPLncPP-DKPN is the first computational method for the interactions prediction between lncRNA-encoded peptide and protein.