@inproceedings{14e9aedf12bc4d3ba0c21cf3c1511344,
title = "Learning Bayesian networks with biomedical applications",
abstract = "This talk presents a brief overview of methods for learning Bayesian networks. It discusses on recent methods and theoretical results to speed up computations and to improve accuracy, leading to an approach which can deal with many thousands of variables. Applications arising in biomedical problems are described, where it is argued that Bayesian networks can provide meaningful and interpretable results. In particular, we discuss on the use of Bayesian networks for data imputation, unsupervised clustering and classification using high-dimensional data sets of lymphoma patients.",
keywords = "Bayesian networks, Clustering, Data imputation, Structure learning",
author = "{de Campos}, {Cassio P.}",
note = "Included in the Preface of Springer LNAI 9505; 2nd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015 ; Conference date: 16-11-2015 Through 18-11-2015",
year = "2015",
doi = "10.1007/978-3-319-28379-1",
language = "English",
isbn = "9783319283784",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
editor = "Joe Suzuki and Maomi Ueno",
booktitle = "Advanced Methodologies for Bayesian Networks - 2nd International Workshop, AMBN 2015, Proceedings",
address = "Germany",
}