Multiple protein feature prediction with statistical relational learning
This work addresses the gap between sequenced and annotated biological data for biologists, but it is incremental as it builds on existing methods with a specific enhancement.
The authors tackled the problem of automatic protein feature prediction by introducing a novel approach using Semantic Based Regularization to incorporate prior knowledge, resulting in improved prediction quality on the yeast genome.
High throughput sequencing techniques have highly impactedon modern biology, widening the gap between sequenced andannotated data. Automatic annotation tools are thereforeof the foremost importance to guide biologists' experiments. However, most of the state-of-the-art methods rely on annotation transfer, offering reliable predictions only in homology settings. In this work we present a novel appraoch to protein feature prediction, which exploits the Semanti Based Regularization to inject prior knowledge in the learning process. The experimental results conducted on the yeast genome show that the introduction of the constraints positively impacts on the overall prediction quality.