Predicting Disease Genes Using Connectivity and Functional Features

Image credit: [Lorenzo Madeddu]


We predict disease-genes relations on the human interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. To exploit at best latent information in the network, we propose an extended version of random walks, named Random Watcher-Walker (RW²), which is shown to perform better than other state-of-the-art algorithms. We also show that performance of RW² and other compared state-of-the-art algorithms is extremely sensitive to the interactome used, and to the adopted disease categorizations, since this influences the ability to capture regularities in presence of sparsity and incompleteness.

In IEEE International Conference on Bioinformatics and Biomedicine
Lorenzo Madeddu
Lorenzo Madeddu
Senior data scientist (R&D), PhD

He is a senior data scientist (R&D) in the Knowledge Graph Insights team at AstraZeneca.