Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Similar to in the social networks analysis domain, diffusion-based low-dimensional network embedding methods have proved quite powerful for biological networks. In particular, these methods have been highly successful in creating coherent local neighborhoods that correlate to gene function, enabling the downstream use of the entire machine learning toolbox to perform multiple inference tasks on these networks. We design methods that take into account protein sequence, structure or evolution and combine with network inference in order to predict genes and pathways important in diseases.

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