Measuring Functional Distances in Gene Ontology
Author Information
Author(s): Angela del Pozo, Florencio Pazos, Alfonso Valencia
Primary Institution: Spanish National Cancer Research Centre (CNIO)
Hypothesis
Can functional distances between Gene Ontology terms be quantified using co-occurrence in protein databases?
Conclusion
The proposed method quantifies functional similarities between Gene Ontology terms and enhances protein function comparison.
Supporting Evidence
- The method provides a new definition of distance that enables the similarity between GO terms to be quantified.
- The Functional Tree allows for the clustering of GO terms into biologically meaningful groups.
- The approach could enhance function-based protein searches in databases.
Takeaway
This study created a way to measure how similar different protein functions are by looking at how often they appear together in data.
Methodology
The method uses co-occurrence of Gene Ontology terms in protein databases to create a similarity matrix and applies spectral clustering to define functional distances.
Potential Biases
The reliance on existing databases may introduce biases in functional associations.
Limitations
The method relies on a limited set of annotations, resulting in low coverage of the total number of terms in the ontology.
Digital Object Identifier (DOI)
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