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

TL;DR

This paper introduces CausNet, a dynamic programming algorithm with generational orderings for efficiently finding optimal Bayesian networks in high-dimensional data, supporting various data types and outperforming existing methods.

Contribution

The paper presents a novel generational orderings based search algorithm that significantly reduces search space and enables scalable, optimal Bayesian network discovery in large datasets.

Findings

Performs better than three state-of-the-art algorithms in simulations.

Successfully applied to ovarian cancer gene expression data with 513 genes.

Finds optimal networks with 6 genes in minutes on standard hardware.

Abstract

Finding a globally optimal Bayesian Network using exhaustive search is a problem with super-exponential complexity, which severely restricts the number of variables that it can work for. We implement a dynamic programming based algorithm with built-in dimensionality reduction and parent set identification. This reduces the search space drastically and can be applied to large-dimensional data. We use what we call generational orderings based search for optimal networks, which is a novel way to efficiently search the space of possible networks given the possible parent sets. The algorithm supports both continuous 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-art algorithms that are currently used extensively. We then apply it to an Ovarian Cancer gene expression dataset with 513 genes and a survival outcome. Our algorithm is able to find an optimal network describing the disease pathway consisting of 6 genes leading to the outcome node in a few minutes on a basic computer. Our generational orderings based search for optimal networks, is both efficient 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 the number of nodes and density of the networks. Availability of two scoring option-BIC and Bge-and implementation of survival outcomes and mixed data types makes our algorithm very suitable for many types of high dimensional biomedical data to find disease pathways.