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Finding a globally optimal Bayesian Network using exhaustive search is aproblem with super-exponential complexity, which severely restricts the numberof variables that it can work for. We implement a dynamic programming basedalgorithm with built-in dimensionality reduction and parent set identification.This reduces the search space drastically and can be applied tolarge-dimensional data. We use what we call generational orderings based searchfor optimal networks, which is a novel way to efficiently search the space ofpossible networks given the possible parent sets. The algorithm supports bothcontinuous and categorical data, and categorical as well as survival outcomes.We demonstrate the efficacy of our algorithm on both synthetic and real data.In simulations, our algorithm performs better than three state-of-artalgorithms that are currently used extensively. We then apply it to an OvarianCancer gene expression dataset with 513 genes and a survival outcome. Ouralgorithm is able to find an optimal network describing the disease pathwayconsisting of 6 genes leading to the outcome node in a few minutes on a basiccomputer. Our generational orderings based search for optimal networks, is bothefficient and highly scalable approach to finding optimal Bayesian Networks,that can be applied to 1000s of variables. Using specifiable parameters -correlation, FDR cutoffs, and in-degree - one can increase or decrease thenumber of nodes and density of the networks. Availability of two scoringoption-BIC and Bge-and implementation of survival outcomes and mixed data typesmakes our algorithm very suitable for many types of high dimensional biomedicaldata to find disease pathways.

CausNet : Generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints