Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem [PDF]
Entropy, 2021 The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In
Enrique G. Rodrigo +3 more
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Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models. [PDF]
J Neurosci, 2021 Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the
Carrillo-Reid L +6 more
europepmc +2 more sources
Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings. [PDF]
Neuropsychopharmacology, 2021 Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy.
Athreya AP +16 more
europepmc +2 more sources
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis. [PDF]
Neuropsychologia, 2020 With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years.
Cai MB +4 more
europepmc +3 more sources
From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning [PDF]
IEEE Access, 2020 Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning.
Ivan Glasser +2 more
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Inference Attacks on Genomic Data Based on Probabilistic Graphical Models
Big Data Mining and Analytics, 2020 The rapid progress and plummeting costs of human-genome sequencing enable the availability of large amount of personal biomedical information, leading to one of the most important concerns — genomic data privacy. Since personal biomedical data are highly
Zaobo He, Junxiu Zhou
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Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans. [PDF]
Philos Trans R Soc Lond B Biol Sci, 2018 We propose an approach to represent neuronal network dynamics as a probabilistic graphical model (PGM). To construct the PGM, we collect time series of neuronal responses produced by the neuronal network and use singular value decomposition to obtain a ...
Liu H, Kim J, Shlizerman E.
europepmc +2 more sources
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models [PDF]
Physical Review X, 2017 Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
Marcello Benedetti +3 more
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The potential of probabilistic graphical models in linkage map construction. [PDF]
Theor Appl Genet, 2017 Probabilistic graphical models show great potential for robust and reliable construction of linkage maps. We show how to use probabilistic graphical models to construct high-quality linkage maps in the face of data perturbations caused by genotyping ...
Wang H, van Eeuwijk FA, Jansen J.
europepmc +2 more sources
Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models [PDF]
Sensors, 2016 Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure.
Jaka Kravanja +4 more
doaj +2 more sources