Abstract
Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.
Highlights
Molecules in a living cell interact with each other in a coordinated fashion to carry out important biological functions
We present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks
We first set out to test whether the model assumptions of GBNs are too restrictive to be effectively applied to real biological data
Summary
Molecules in a living cell interact with each other in a coordinated fashion to carry out important biological functions. Recent advances in high-throughput technologies have given rise to numerous algorithms for reverse-engineering interaction networks from molecular observations, as they provide an efficient and systematic way of analyzing the molecular state of a large number of genes. One class of such interaction networks that has generated much interest in recent years is transcriptional gene regulatory networks, which specify the set of genes that influence a given gene’s expression level. This type of pattern can be naturally modeled in a causal graph or Bayesian network.
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