Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related quality of life in Parkinson's disease. Most of these challenging problems posed by neuroscience involve new Bayesian network designs that can cope with multiple class variables, small sample sizes, or labels annotated by several experts
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
The classification of biological neuron types and networks poses challenges to the full understandin...
Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis...
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In...
Neuronal morphology is hugely variable across brain regions and species, and their classification st...
International audienceNetwork connectivity fingerprints are among today's best choices to obtain a f...
Studying interactions between different brain regions or neural components is crucial in understandi...
Alzheimer’s disease (AD) is a fatal neurodegenerative disease that induces deficits in multiple cogn...
Neuron morphology is crucial for neuronal connectivity and brain information processing. Computation...
A class-bridge decomposable multidimensional Gaussian net- work is presented as an interpretable an...
Neuroscientists have shown increased interest in knowing interactions among brain regions activated ...
AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences i...
We employ the language of Bayesian networks to systematically construct gene-regulation topologies f...
This article reviews current advances and developments in neural networks. This requires recalling s...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
The classification of biological neuron types and networks poses challenges to the full understandin...
Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis...
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In...
Neuronal morphology is hugely variable across brain regions and species, and their classification st...
International audienceNetwork connectivity fingerprints are among today's best choices to obtain a f...
Studying interactions between different brain regions or neural components is crucial in understandi...
Alzheimer’s disease (AD) is a fatal neurodegenerative disease that induces deficits in multiple cogn...
Neuron morphology is crucial for neuronal connectivity and brain information processing. Computation...
A class-bridge decomposable multidimensional Gaussian net- work is presented as an interpretable an...
Neuroscientists have shown increased interest in knowing interactions among brain regions activated ...
AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences i...
We employ the language of Bayesian networks to systematically construct gene-regulation topologies f...
This article reviews current advances and developments in neural networks. This requires recalling s...
Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning t...
The overarching purpose of the studies presented in this report is the exploration of the uses of in...
The classification of biological neuron types and networks poses challenges to the full understandin...
Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis...