Identifying disease genes from PPI networks weighted by gene expression under different conditions

The identification of disease genes is an essential issue to decipher the mechanisms of complex diseases. Many existing methods combine machine learning algorithms and network information to predict disease genes and are based on the `guilt by association' assumption, where disease genes are considered to be close to each other in a biomolecular network. Although these methods have gained many novel findings, most of them ignored the edge dynamic changes of biomolecular networks under different conditions when only utilizing the `guilt by association' principle, which will limit their performance. To address this problem, we propose an algorithm that combines the `guilt by association' and the `guilt by rewiring' of biomolecular networks at the same time. The difference of gene co-expression between case and control samples are first processed to obtain the edge dynamic changes (rewiring) of biomolecular networks through weighting the edges of protein-protein interaction (PPI) networks. Then, features are extracted from the weighted PPI network. Finally, a logistic regression is adopted to identify the disease genes. The algorithm achieves AUC values of 0.95, 0.90 and 0.92 on the identification of breast-cancer-related, lung-cancer-related and schizophrenia-related genes, respectively. Two new schizophrenia-related genes are also found from the ranked unknown genes list.