Results 221 to 230 of about 151,311 (270)

Prediction Model for Low Bone Mineral Density in Cancer Survivors and Age-Matched Controls Using a Causal Bayesian Network: A Nationwide Population-Based Study in Korea. [PDF]

Clin Med Insights Oncol
Han S, Park SB, Oh S, Oh B.
europepmc   +1 more source

Integrating Surveillance and Stakeholder Insights to Predict Influenza Epidemics: A Bayesian Network Study in Queensland, Australia. [PDF]

Int J Environ Res Public Health
Sahin O, Phung H, Standke A, Rajmokan M, Raulli A, York A, Lee P.   +6 more
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Cross-population amplitude coupling in high-dimensional oscillatory neural time series. [PDF]

Front Comput Neurosci
Bong H, Ventura V, Yttri EA, Smith MA, Kass RE.   +4 more
europepmc   +1 more source

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Probabilistic Graphical Models

2021
The most important problem in machine learning is to estimate and infer the value of unknown variables (e.g., class label) based on the observed evidence (e.g., training samples). Probabilistic models provide a framework that considers learning problems as computing the probability distributions of variables.
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Probabilistic Graphical Modeling under Heterogeneity

2023
AbstractProbabilistic graphical models are powerful and widely used tools to quantify, visualize and interpret dependencies in complex biological systems such as highthroughput genomics and proteomics. However, most existing graphical modeling methods assume homogeneity within and across samples which restricts their broad applicability to cases where ...
Liying Chen, Satwik Acharyya, Chunyu Luo, Yang Ni, Veerabhadran Baladandayuthapani   +4 more
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Probabilistic Graphical Models for Computational Biomedicine

Methods of Information in Medicine, 2003
Summary Background: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics are gradually converging into a larger field that we call computational biomedicine. Objectives: Developing a computational framework that is common to the different disciplines that compose computational biomedicine ...
Y, Moreau, P, Antal, G, Fannes, B, De Moor   +3 more
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Probabilistic Graphical Models of Dyslexia

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naeve Bayes models which differ by their assumptions about the generation process ...
Yair Lakretz, Gal Chechik, Naama Friedmann, Michal Rosen-Zvi   +3 more
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