Extracting protein-protein interactions from MEDLINE using the Hidden Vector State model

Zhou, Deyu; He, Yulan and Kwoh, Chee Keong (2008). Extracting protein-protein interactions from MEDLINE using the Hidden Vector State model. International Journal of Bioinformatics Research and Applications, 4(1) pp. 64–80.

DOI: https://doi.org/10.1504/IJBRA.2008.017164

Abstract

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About