Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Speculative Markov Blanket Discovery for Optimal Feature Selection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning Bayesian Networks
Consistent Feature Selection for Pattern Recognition in Polynomial Time
The Journal of Machine Learning Research
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Scalable, efficient and correct learning of markov boundaries under the faithfulness assumption
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Incremental Bayesian Network Learning for Scalable Feature Selection
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours
Computers in Biology and Medicine
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This paper discusses the application of a novel feature subset selection method in high-dimensional genomic microarray data on type 2 diabetes based on recent Bayesian network learning techniques. We report experiments on a database that consists of 22,283 genes and only 143 patients. The method searches the genes that are conjunctly the most associated to the diabetes status. This is achieved in the context of learning the Markov boundary of the class variable. Since the selected genes are subsequently analyzed further by biologists, requiring much time and effort, not only model performance but also robustness of the gene selection process is crucial. Therefore, we assess the variability of our results and propose an ensemble technique to yield more robust results. Our findings are compared with the genes that were associated with an increased risk of diabetes in the recent medical literature. The main outcomes of the present research are an improved understanding of the pathophysiology of obesity, and a clear appreciation of the applicability and limitations of Markov boundary learning techniques to human gene expression data.