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Neural network-based approaches for biomedical relation classification: A review.

Yijia Zhang¹, Hongfei Lin¹, Zhihao Yang¹

¹School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China.

Journal of Biomedical Informatics

|September 27, 2019

Summary

This summary is machine-generated.

This review explores neural network methods for biomedical relation classification, extracting knowledge like protein-protein interactions from text. These advanced techniques show state-of-the-art performance in classifying biomedical relationships.

Keywords:

Biomedical literature Biomedical relation classification Deep learning Natural language processing Neural networks

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Area of Science:

Biomedical Informatics
Natural Language Processing
Computational Biology

Background:

Biomedical literature growth necessitates automated knowledge extraction.
Protein-protein interactions (PPIs) and drug-drug interactions (DDIs) are key knowledge types.
Unstructured text poses a challenge for accessing this information.

Purpose of the Study:

To review recent advancements in neural network-based approaches for biomedical relation classification.
To summarize corpora, evaluation metrics, and frameworks for this task.
To discuss challenges and future directions in the field.

Main Methods:

Focus on neural network methodologies, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Utilize pretrained word embedding resources for enhanced performance.
Describe general frameworks for neural network-based biomedical relation extraction.

Main Results:

Neural network approaches have achieved state-of-the-art performance on public datasets.
Significant progress has been made in automated biomedical relation classification over the past decade.
These methods offer great benefits for biomedical research and applications.

Conclusions:

Neural networks, including CNNs and RNNs, are effective for classifying biomedical relations.
Further research is needed to address remaining challenges.
Future directions include refining models and expanding applications.