ArticleIEEE transactions on pattern analysis and machine intelligence2013
A sparse structure learning algorithm for Gaussian Bayesian Network identification from high-dimensional data.
Article in IEEE transactions on pattern analysis and machine intelligence, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo.Frontiers in genetics · 2021Article
- Reconstruction of a directed acyclic graph with intervention.Electronic journal of statistics · 2020Article
- Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data.IEEE transactions on pattern analysis and machine intelligence · 2016Article
- From big data analysis to personalized medicine for all: challenges and opportunities.BMC medical genomics · 2015Review
- Directed progression brain networks in Alzheimer's disease: properties and classification.Brain connectivity · 2014Article
- Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2013Article
- Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers.IEEE transactions on big dataArticle
- A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems.Technometrics : a journal of statistics for the physical, chemical, and engineering sciencesArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph--a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.
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Registered trials
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