Gradient Boosting over Linguistic-Pattern-Structured Trees for Learning Protein-Protein Interaction in the Biomedical Literature

Abstract

Protein-based studies contribute significantly to gathering functional information about biological systems; therefore, the protein-protein interaction detection task is one of the most researched topics in the biomedical literature. To this end, many state-of-the-art systems using syntactic tree kernels (TK) and deep learning have been developed. However, these models are computationally complex and have limited learning interpretability. In this paper, we introduce a linguistic-pattern-representation-based Gradient-Tree Boosting model, i.e., LpGBoost. It uses linguistic patterns to optimize and generate semantically relevant representation vectors for learning over the gradient-tree boosting. The patterns are learned via unsupervised modeling by clustering invariant semantic features. These linguistic representations are semi-interpretable with rich semantic knowledge, and owing to their shallow representation, they are also computationally less expensive. Our experiments with six protein-protein interaction (PPI) corpora demonstrate that LpGBoost outperforms the SOTA tree-kernel models, as well as the CNN-based interaction detection studies for BioInfer and AIMed corpora.