title: A tree-like Bayesian structure learning algorithm for small-sample datasets from complex biological model systems.
abstract: BACKGROUND
  There are increasing efforts to bring high-throughput systems biology techniques to bear on complex animal model systems, often with a goal of learning about underlying regulatory network structures (e.g., gene regulatory networks). However, complex animal model systems typically have significant limitations on cohort sizes, number of samples, and the ability to perform follow-up and validation experiments. These constraints are particularly problematic for many current network learning approaches, which require large numbers of samples and may predict many more regulatory relationships than actually exist.
  
  
  RESULTS
  Here, we test the idea that by leveraging the accuracy and efficiency of classifiers, we can construct high-quality networks that capture important interactions between variables in datasets with few samples. We start from a previously-developed tree-like Bayesian classifier and generalize its network learning approach to allow for arbitrary depth and complexity of tree-like networks. Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value. We develop and demonstrate a resampling-based approach to enable the identification of a viable root for the learned tree-like network, important for cases where the root of a network is not known a priori. We also develop and demonstrate an integrated resampling-based approach to the reduction of variable space for the learning of the network. Finally, we demonstrate the utility of this approach via the analysis of a transcriptional dataset of a malaria challenge in a non-human primate model system, Macaca mulatta, suggesting the potential to capture indicators of the earliest stages of cellular differentiation during leukopoiesis.
  
  
  CONCLUSIONS
  We demonstrate that by starting from effective and efficient approaches for creating classifiers, we can identify interesting tree-like network structures with significant ability to capture the relationships in the training data. This approach represents a promising strategy for inferring networks with high positive predictive value under the constraint of small numbers of samples, meeting a need that will only continue to grow as more high-throughput studies are applied to complex model systems.
authors: Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, P. R. China. wwyin@mail.zju.edu.cn.|School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA, 30332-0100, USA. wwyin@mail.zju.edu.cn.
authors: Weiwei
authors: W
authors: Yin
authors: School of Biology, Georgia Institute of Technology, Atlanta, GA, USA. swetha.garimalla@gatech.edu.
authors: Swetha
authors: S
authors: Garimalla
authors: Division of Infectious Diseases, Emory Vaccine Center, Yerkes National Primate Research Center, Emory University School of Medicine, Emory University, Atlanta, GA, USA. camoren@emory.edu.
authors: Alberto
authors: A
authors: Moreno
authors: Division of Infectious Diseases, Emory Vaccine Center, Yerkes National Primate Research Center, Emory University School of Medicine, Emory University, Atlanta, GA, USA. Mary.Galinski@emory.edu.
authors: Mary R
authors: MR
authors: Galinski
authors: School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, 311 Ferst Drive NW, Atlanta, GA, 30332-0100, USA. Mark.Styczynski@chbe.gatech.edu.
authors: Mark P
authors: MP
authors: Styczynski
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D020869:Gene Expression Profiling; D008253:Macaca mulatta; D000069550:Machine Learning; D008288:Malaria / Q000235:genetics; D049490:Systems Biology / Q000379:methods*; D014158:Transcription, Genetic