Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis
- 16 March 2021
- journal article
- research article
- Published by Elsevier BV in Journal of Biomedical Informatics
- Vol. 117, 103730
- https://doi.org/10.1016/j.jbi.2021.103730
Abstract
No abstract availableFunding Information
- Foundation for Science and Technology
This publication has 41 references indexed in Scilit:
- Clinical diagnosis and management of amyotrophic lateral sclerosisNature Reviews Neurology, 2011
- Use of respiratory function tests to predict survival in amyotrophic lateral sclerosisAmyotrophic Lateral Sclerosis, 2010
- ALSFRS-R score and its ratio: A useful predictor for ALS-progressionJournal of the Neurological Sciences, 2008
- Dynamic Bayesian networks as prognostic models for clinical patient managementJournal of Biomedical Informatics, 2008
- Management of respiration in MND/ALS patients: An evidence based reviewAmyotrophic Lateral Sclerosis, 2006
- The ALSFRSr predicts survival time in an ALS clinic populationNeurology, 2005
- A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray dataBioinformatics, 2004
- Learning Bayesian networks: The combination of knowledge and statistical dataMachine Learning, 1995
- A Bayesian method for the induction of probabilistic networks from dataMachine Learning, 1992
- Approximating discrete probability distributions with dependence treesIEEE Transactions on Information Theory, 1968