Benchmarking Methods For Predicting Phenotype Gene Associations

Assigning human genes to diseases and related phenotypes is an important topic in modern genomics. Human Phenotype Ontology (HPO) is a standardized vocabulary of phenotypic abnormalities that occur in human diseases. Computational methods such as label-propagation and supervised-learning address challenges posed by traditional approaches such as manual curation to link genes to phenotypes in the HPO. It is only in recent years that computational methods have been applied in a network-based approach for predicting genes to disease-related phenotypes. In this thesis, we present an extensive benchmarking of various computational methods for the task of network-based gene classification. These methods are evaluated on multiple protein interaction networks and feature representations. We empirically evaluate the performance of multiple prediction tasks using two evaluation experiments: cross-fold validation and the more stringent temporal holdout. We demonstrate that all of the prediction methods considered in our benchmarking analysis have similar performance, with each of the methods outperforming a random predictor.