Efficient Gene Community Search to Discover Similar Aspects for Similarity Explanation

Gene similar aspects provide reliable explanation in understanding the biological roles and gene functions. As the volume of biomedical data expands, most of the current methods for similar explanation among genes are no longer applicable. Limited by information sources and search effciency, these methods cannot be flexible and effcient for the similarity analysis. We hereby propose a flexible method VENUS to analyze gene similar aspect among multiple genes on heterogeneous information networks, which constructed from public biomedicine databases and literature. VENUS infers the semantic and structural similarity of the query genes by gene community search. In this way, VENUS narrows the search space when searching information network within an acceptable time cost. Besides, VENUS is not limited by inherent domain knowledge and is adaptive to large-scale networks. Through experiments on multiple different public data sources, it demonstrates that VENUS is effective and effcient.