New Prediction Method for Regulatory Genes of Complex Disease, Published in ‘Genome Research’ A new method to effectively detect the genes that control complex diseases like cancer and diabetes has been developed by Professor Lee In-seok and his biotechnology team. This research has been recently published in the online version of a top ranked journal of genomics ‘Genome Research’. As an international joint research, Professor Lee In-seok conducted this study with Ben Lehner European Molecular Biology Laboratory; Edward Marcotte, Professor of Biochemistry at the University of Texas; Professor Fraser at the University of Toronto; and Shin Joon-ha, Yonsei graduate student. The team demonstrated that a regulatory gene complex can be discovered by a bioinformation-based model called the ‘functional gene network’, which is more cost-effective than the existing random search or the information-based prediction model. More than 95% of human diseases are known to be complex diseases (rather than Mendelian or single-gene diseases) which are generated from the interaction of many disease-related genes. In the case of cancer, a typical complex disease, 300 ~ 600 related genes have been found to exist; and uncovering the intricate interactions of them is believed to be an important key to conquest the disease. With the existing random search, it is impossible to construct a map of the interactions between the regulatory genes. But the recent joint research project has developed a prediction method on the basis of the functional gene network.