Machine Learning - Special issue on learning with probabilistic representations
A Bayesian network approach to explaining time series with changing structure
Intelligent Data Analysis
A spatio-temporal Bayesian network classifier for understanding visual field deterioration
Artificial Intelligence in Medicine
Learning the structure of dynamic probabilistic networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bayesian network classifiers for time-series microarray data
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Modelling and analysing the dynamics of disease progression from cross-sectional studies
Journal of Biomedical Informatics
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The progression of many biological and medical processes such as disease and development are inherently temporal in nature. However many datasets associated with such processes are from cross-section studies, meaning they provide a snapshot of a particular process across a population, but do not actually contain any temporal information. In this paper we address this by constructing temporal orderings of crosssection data samples using minimum spanning tree methods for weighted graphs. We call these reconstructed orderings pseudo time-series and incorporate them into temporal models such as dynamic Bayesian networks. Results from our preliminary study show that including pseudo temporal information improves classification performance. We conclude by outlining future directions for this research, including considering different methods for time-series construction and other temporal modelling approaches.