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Link Prediction in Biological Networks using Multi-Mode Exponential Random Graph Models

We propose a novel multi-mode exponential random graph model for supervised prediction of gene networks, coupled with a penalized estimation framework for improved prediction performance. The proposed framework facilitates the analysis of gene networks with multiple edge types, and provides a systematic framework for incorporating multiple sources of biological data, as well as diverse attributes regarding function and location of genes, and structure of observed networks. Results of numerical experiments indicate that the method enjoys superior performance compared to other state-of-the-art reconstruction methods.