Knowledge-aware Attention Network for Protein-Protein Interaction Extraction
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extraction methods need extensive feature engineering and cannot make full use of the prior knowledge in knowledge bases (KB). KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in PPI extraction. This paper proposes a knowledge-aware attention network (KAN) to fuse prior knowledge about protein-protein pairs and context information for PPI extraction. The proposed model first adopts a diagonal-disabled multi-head attention mechanism to encode context sequence along with knowledge representations learned from KB. Then a novel multi-dimensional attention mechanism is used to select the features that can best describe the encoded context. Experiment results on the BioCreative VI PPI dataset show that the proposed approach could acquire knowledge-aware dependencies between different words in a sequence and lead to a new state-of-the-art performance.
Code (1)
Tasks
Feature EngineeringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction
Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficult setting arises when candidate interactions include proteins that hav…
Graph Representation LearningKnowledge GraphsGraph LearningPrompt-Guided Injection of Conformation to Pre-trained Protein Model
Pre-trained protein models (PTPMs) represent a protein with one fixed embedding and thus are not capable for diverse tasks. For example, protein structures can shift, namely protein folding, between several conformations…
Language ModelingLanguage ModellingMasked Language ModelingProtein FoldingDocking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration
Computational prediction of enzymatic reactions represents a crucial challenge in sustainable chemical synthesis across various scientific domains, ranging from drug discovery to materials science and green chemistry. Th…
Drug DiscoveryMolecular DockingPredictionImproving Neural Protein-Protein Interaction Extraction with Knowledge Selection
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured informatio…
RelationOntoProtein: Protein Pretraining With Gene Ontology Embedding
Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of dive…
Contrastive LearningKnowledge GraphsOntology EmbeddingProtein Function Prediction