PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology

The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our model. The document to be analyzed is first split into sentences and annotated with a base method to generate candidate concepts. The sentences, along with the candidate concepts, are then fed into the pre-trained model for re-ranking. Our model comprises the pre-trained BlueBERT and a feature selection module, followed by a contrastive loss. We re-ranked the results generated by three robust HPO annotation tools and compared the performance against most of the existing approaches. The experimental results show that our model can improve the performance of the existing methods. Significantly, it boosted 3.0% and 5.6% in F1 score on the two evaluated datasets compared with the base methods. It removed more than 80% of the false positives predicted by the base methods, resulting in up to 18% improvement in precision. Our model utilizes the descriptive data in the ontology and the contextual information in the sentences for re-ranking. The results indicate that the additional information and the re-ranking model can significantly enhance the precision of HPO concept recognition compared with the base method.