Patrick Danaher
Biology is increasingly moving beyond an atomistic, single-protein or single-gene scope to work on a systems level, examining the behavior of biological networks. Modern high-dimensional data allow quick estimation of network structures that before would have required years of work in the lab. We propose the Joint Graphical Lasso, a method for estimation of biological networks across multiple classes of data, for example in cancer and normal tissue. I will review the principles of network estimation, introduce our proposal for extending estimation to multiple classes of data, describe its solution using convex optimization, and describe analyses of real and simulated datasets.
Keywords: graphical lasso, networks, gene expression, penalized likelihood, gradient descent
