RGFinder: a system for determining semantically related genes using GO graph minimum spanning tree

Predicting the functions of a protein from its ability to associate with other molecules

BACKGROUND

All proteins associate with other molecules. These associated molecules are highly predictive of the potential functions of proteins. The association of a protein and a molecule can be determined from their co-occurrences in biomedical abstracts. Extensive semantically related co-occurrences of a protein's name and a molecule's name in the sentences of biomedical abstracts can be considered as indicative of the association between the protein and the molecule. Dependency parsers extract textual relations from a text by determining the grammatical relations between words in a sentence. They can be used for determining the textual relations between proteins and molecules. Despite their success, they may extract textual relations with low precision. This is because they do not consider the semantic relationships between terms in a sentence (i.e., they consider only the structural relationships between the terms). Moreover, they may not be well suited for complex sentences and for long-distance textual relations.

RESULTS

We introduce an information extraction system called PPFBM that predicts the functions of unannotated proteins from the molecules that associate with these proteins. PPFBM represents each protein by the other molecules that associate with it in the abstracts referenced in the protein's entries in reliable biological databases. It automatically extracts each co-occurrence of a protein-molecule pair that represents semantic relationship between the pair. Towards this, we present novel semantic rules that identify the semantic relationship between each co-occurrence of a protein-molecule pair using the syntactic structures of sentences and linguistics theories. PPFBM determines the functions of an un-annotated protein p as follows. First, it determines the set S r of annotated proteins that is semantically similar to p by matching the molecules representing p and the annotated proteins. Then, it assigns p the functional category FC if the significance of the frequency of occurrences of S r in abstracts associated with proteins annotated with FC is statistically significantly different than the significance of the frequency of occurrences of S r in abstracts associated with proteins annotated with all other functional categories. We evaluated the quality of PPFBM by comparing it experimentally with two other systems. Results showed marked improvement.

CONCLUSIONS

The experimental results demonstrated that PPFBM outperforms other systems that predict protein function from the textual information found within biomedical abstracts. This is because these system do not consider the semantic relationships between terms in a sentence (i.e., they consider only the structural relationships between the terms). PPFBM's performance over these system increases steadily as the number of training protein increases. That is, PPFBM's prediction performance becomes more accurate constantly, as the size of training proteins gets larger. This is because every time a new set of test proteins is added to the current set of training proteins. A demo of PPFBM that annotates each input Yeast protein (SGD (Saccharomyces Genome Database). Available at: http://www.yeastgenome.org/download-data/curation) with the functions of Gene Ontology terms is available at: (see Appendix for more details about the demo) http://ecesrvr.kustar.ac.ae:8080/PPFBM/.

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.