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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Improving neural protein-protein interaction extraction with knowledge selection.

Huiwei Zhou¹, Xuefei Li¹, Weihong Yao¹

¹School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.

Computational Biology and Chemistry

|November 11, 2019

Summary

This summary is machine-generated.

This study introduces a Knowledge Selection Model (KSM) for protein-protein interaction extraction. KSM selectively uses prior knowledge, improving precision medicine by enhancing protein relation discovery.

Keywords:

Knowledge selection Mutual attention PPI extraction Prior knowledge

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Area of Science:

Biomedical Informatics
Computational Biology
Bioinformatics

Background:

Protein-protein interactions (PPIs) are crucial for understanding cellular mechanisms and advancing precision medicine.
Knowledge bases (KBs) offer structured protein data, but effective integration into PPI extraction remains challenging.
Prior knowledge from KBs needs context-specific selection for optimal utility in relation extraction.

Purpose of the Study:

To develop a novel Knowledge Selection Model (KSM) for improved protein-protein interaction (PPI) extraction.
To effectively fuse context-specific prior knowledge with contextual information for enhanced PPI extraction.
To advance the application of KBs in supporting precision medicine through better PPI identification.

Main Methods:

Utilized two Transformers to encode protein pair context sequences, generating protein embeddings.
Implemented a mutual attention mechanism to capture salient context features relevant to protein pairs.
Developed a knowledge selector to distill relation embeddings based on context features.
Concatenated selected relation embeddings with context features for final PPI extraction.

Main Results:

The proposed Knowledge Selection Model (KSM) achieved state-of-the-art performance on the BioCreative VI PPI dataset.
KSM attained a 38.08% F1-score, demonstrating significant improvement in PPI extraction accuracy.
The integration of knowledge selection proved effective in enhancing the model's ability to identify protein relations.

Conclusions:

The Knowledge Selection Model (KSM) offers a novel and effective approach for protein-protein interaction extraction.
Selective utilization of prior knowledge from knowledge bases is critical for context-aware PPI extraction.
This method holds promise for advancing precision medicine by improving the extraction of protein relationships from scientific literature.