Novelty indicator for enhanced prioritization of predicted gene ontology annotations

Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritize their results, we introduce a new element that can improve accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of “originality” of the annotations predicted for a specific gene to Gene Ontology (GO) terms. This indicator, joint with our previously introduced prediction steps, helps by prioritizing the most novel interesting annotations predicted. We performed an accurate biological functional analysis of the prioritized annotations predicted with high accuracy by our indicator and previously proposed methods. The relevance of our biological findings proves effectiveness and trustworthiness of our indicator and of its prioritization of predicted annotations.