Ontology-Based Prediction and Prioritization of Gene Functional Annotations


Genes and their protein merchandise are essential molecular units of a living organism. The knowledge of their functions is vital for the understanding of physiological and pathological biological processes, plus in the event of new medicine and therapies. The association of a gene or protein with its functions, described by controlled terms of biomolecular terminologies or ontologies, is known as gene functional annotation. Very several and valuable gene annotations expressed through terminologies and ontologies are accessible. Nevertheless, they might embody some erroneous data, since solely a subset of annotations are reviewed by curators. Furthermore, they are incomplete by definition, given the rapidly evolving pace of biomolecular information. During this situation, computational methods that will be able to quicken the annotation curation process and reliably suggest new annotations are terribly vital. Here, we have a tendency to initial propose a computational pipeline that uses different semantic and Machine Learning ways to predict novel ontology-primarily based gene functional annotations; then, we introduce a brand new semantic prioritization rule to categorize the expected annotations by their chance of being correct. Our tests and validations proved the effectiveness of our pipeline and prioritization of predicted annotations, by selecting as presumably manifold predicted annotations that were later confirmed.

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