Since biological networks have been found to be comparable to communication and social networks (28) through commonalities such as scale-freeness and small-world properties, we reasoned that the algorithms used for social and Web networks should be equally applicable to biological networks and developed ToppNet (27). One of the earliest efforts (24) uses a classifier based on several topological features, including degree (number of links to the protein), 1N index (proportion of links to disease-related proteins), 2N index (average 1N index in the neighbors), average distance to disease genes and positive topology coefficient (average neighborhood overlapping with disease genes). A more recent application, Genes2Networks (29), identifies important genes based on a list of ‘seed’ genes. It generates a Z-score for each ‘intermediate’ gene from a binomial proportions test to represent its specificity or significance to the ‘seed’ genes. The former method, independent of known disease-related genes, is used for disease candidate gene identification, especially in cases where there is little or no prior knowledge about the disease. The latter application, on the other hand, uses a ‘seed’ list as training to
