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A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting
Steven Cox1, Xialan Dong2, Ruhi Rai1
1Renaissance Computing Institute (RENCI), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Journal of Biomedical Informatics
|October 2, 2020
Summary
This study uses Word2Vec text mining on PubMed articles to predict protein-protein interactions. The method accurately identifies P53-interacting kinases and has broad applications in drug discovery and translational research.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Text Mining
Background:
- Biomedical literature contains vast unstructured data on proteins, ligands, and diseases.
- Systematic analysis of this corpus can lead to discoveries like new protein interactions and drug indications.
Purpose of the Study:
- To investigate a text mining methodology for discovering novel biomedical relationships.
- To apply Word2Vec and semantic similarity for predicting protein-protein interactions.
Main Methods:
- Utilized Word2Vec for deriving word embeddings from PubMed full-text articles.
- Employed semantic similarity comparison, with and without k-Nearest Neighbor (kNN), for relationship prediction.
- Conducted retrospective analyses on a dataset of known P53-interacting proteins.
Main Results:
- Word2Vec semantic similarity successfully inferred functional relatedness among P53-interacting kinases.
- Time-split experiments showed that similarity comparison and kNN models could predict novel P53 interactors.
- Cumulative correct predictions increased over time in time-split experiments.
Conclusions:
- Text mining of biomedical literature using Word2Vec and similarity metrics offers accurate predictions of protein-protein interactions.
- This methodology has significant potential for translational biomedical studies, including drug repurposing and mechanism elucidation.
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