Predicting protein function from biomedical text

We propose a classifier system called PFPBT that predicts the functions of un-annotated proteins. PFPBT assigns an un-annotated protein p the functional category of annotated proteins that are semantically similar to p. Each protein p is represented by a vector of weights. Each weight reflects the significance of a molecule m in the biomedical abstracts associated with p. That is, each weight quantifies the likelihood of the association between m and p. This is because all proteins bind to other molecules, which are highly predictive of the functions of the proteins. Let S be the set of proteins that is semantically similar to an un-annotated protein p. p is annotated with the functional category f, if its occurrence probability in abstracts associated with S whose functional category is f is statistically significantly different than its occurrences in abstracts associated with S that belong to all other functional categories. PFPBT automatically extracts each co-occurrence of a protein-molecule pair that represents semantic relationship between the pair. We present novel semantic rules based on the syntactic structures of sentences for identifying the semantic relationships between each co-occurrence of a protein-molecule pair in a sentence. We evaluated PFPBT by comparing it experimentally with two systems. Results showed improvement.