In network analysis, genes are known to work in groups by their biological functionality, where distinetive groups reveals different gene functionalities.In such a situation, identifying grouping structures as well as informative genes becomes critical in understanding progression of a disease.Motivated from gene network analysis, we investigate, in a regression context, simnltaneous supervised clustering and feature selection over an arbitrary undireeted graph, where each predictor corresponds to one node in the graph and existence of a connecting path between two nodes indicates possible grouping between the two predictors.In this talk, I will review recent developments and discuss computational methods for simultaneous supervised clustering and feature selection over a graph.Numerical examples will be given.in addition to some theoretical aspects of supervised clustering and feature selection.