Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.
A new relaxation strategy is presented in this paper to approximately solve the quadratically and linearly constrained quadratic programming. To improve the conservation of traditional semidefinite relaxation (SDR) strategy, we introduce a new linear constraint, which can be derived from the constraints of original problem, to the SDR problem. Furthermore, a randomization method is provided to extract good feasible solution of original problem from optimal solution of relaxed problem. Some numerical examples show that the proposed method can efficiently improve the performance of the traditional SDR strategy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.