Identifying candidate disease genes using a trace norm constrained bipartite raking model

Computational prediction of genes that play roles in human diseases remains an important but challenging task. In this work, we formulate candidate gene prediction as a bipartite ranking problem combining a task-wise ordered observation model with a latent multitask regression function using the matrix-variate Gaussian process (MV-GP). We then use a trace-norm constrained variational inference approach to obtain the bipartite ranking model variables and the parameters of the underlying multitask regression model. We use this model to predict candidate genes from two gene-disease association data sets and show that our model outperforms current state-of-the-art methods. Finally, we demonstrate the practical utility of our method by successfully recovering well characterized gene-disease associations hidden in our training data.