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Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding
Yuanyuan Zhang1,2, Ziqi Wang1, Shudong Wang2
1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
Frontiers in Genetics
|October 11, 2021
Summary
Graph embedding methods significantly improve protein similarity analysis by incorporating structural information from Gene Ontology (GO) graphs. Random walk methods excel, outperforming traditional information content approaches for proteomics research.
Area of Science:
- Proteomics
- Bioinformatics
- Computational Biology
Background:
- Protein-protein interactions and functions are crucial in proteomics.
- Gene Ontology (GO) provides standardized terms for gene product description.
- Existing methods often overlook GO term structural information.
Purpose of the Study:
- To analyze protein similarity using GO and GO Annotation (GOA) graphs with graph embedding methods.
- To evaluate the performance of different graph embedding techniques.
- To compare graph embedding methods against traditional Information Content (IC)-based approaches.
Main Methods:
- Utilized graph embedding to learn feature vectors for GO terms and proteins.
- Applied Dynamic Time Warping (DTW) on GO graphs and cosine similarity on GOA graphs.
- Conducted link prediction experiments to assess network reliability.
Main Results:
- Graph embedding methods demonstrated superior performance compared to IC-based methods.
- Random walk graph embedding techniques showed excellent results in protein similarity calculation.
- GO(DTW) features proved highly effective for analyzing protein similarity.
Conclusions:
- Graph embedding methods offer significant advantages for protein similarity analysis.
- Incorporating structural information from GO graphs enhances accuracy.
- Random walk embedding and GO(DTW) are promising for future proteomics studies.
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