PhD Research Talk: Gene Prioritization using Diffusion State Distance
In order to understand disease, and find curative measures, we have to know the causal genes of disease. Until now, the genetic causes of many complex diseases are still unknown. Thus identifying the genetic cause of complex disease is a major challenge in human genetics. With the recent explosion in high-throughput experimental techniques, association and linkage studies provide a lot of information, but experimentally testing every causal link between a gene and a disease is time-consuming and expensive. So prioritization of candidate genes prior to experimental testing is necessary to narrow down the candidate search space and reduce the associated costs. Among gene prioritization tools, some exploit the protein-protein interaction network to prioritize genes involved in diseases. We introduce a gene prioritization method - DSD virtual disease node – based on our recently defined Diffusion State Distance (DSD) metric, to facilitate the identification of candidate disease genes. We test our method on 439 disease-gene families, and the result showed our method outperforms one of best gene prioritization tools by 2% based on the area under the ROC curve measure. This is a joint project with Jisoo Park, Donna Slonim, and Lenore Cowen.