DEEPER: a Full Parsing based Approach to Protein Relation Extraction

Publication type
P1
Publication status
Published
Authors
Fayruzov, T., De Cock, M., Cornelis, C., & Hoste, V.
Journal
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Series
Lecture Notes in Computer Science
Volume
4973
Pagination
36-47
Publisher
Springer (Naples, Italy)
Download
(.pdf)

Abstract

Lexical variance in biomedical texts poses a challenge to automatic protein relation mining. We therefore propose a new approach that relies only on more general language structures such as parsing and dependency information for the construction of feature vectors that can be used by standard machine learning algorithms in deciding whether a sentence describes a protein interaction or not. As our approach is not dependent on the use of specific interaction keywords, it is applicable to heterogeneous corpora. Evaluation on benchmark datasets shows that our method is competitive with existing state-of-the-art algorithms for the extraction of protein interactions.