Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology
High performance and accurate protein function prediction is an important problem in molecular biology. Many
contemporary ontologies, such as Gene Ontology (GO), have a hierarchical structure that can be exploited to improve the
prediction accuracy, and lower the computational cost, of protein function prediction. We leverage the hierarchical structure
of the ontology in two ways. First, we present a method of creating hierarchy-aware training sets for machine-learned
classifiers and we show that, in the case of GO molecular function, it is the most accurate method compared to not
considering the hierarchy during training. Second, we use the hierarchy to reduce the computational cost of classification.
We also introduce a sound methodology for evaluating hierarchical classifiers using global cross-validation. Biologists
often use sequence similarity (e.g. BLAST) to identify a “nearest neighbor” sequence and use the database annotations of
this neighbor to predict protein function. In these cases, we use the hierarchy to improve accuracy by a small amount.
When no similar sequences can be found (which is true for up to 40% of some common proteomes), our technique can improve
accuracy by a more significant amount. Although this paper focuses on a specific important application—protein
function prediction for the GO hierarchy—the techniques may be applied to any classification problem over a hierarchical