Results 51 to 60 of about 7,343,069 (347)
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Appropriate, accessible and appealing probabilistic graphical models [PDF]
, 2017 Appropriate - Many multivariate probabilistic models either use independent distributions or dependent Gaussian distributions. Yet, many real-world datasets contain count-valued or non-negative skewed data, e.g.
Inouye, David Iseri
core +2 more sources
Insights into Learning Competence Through Probabilistic Graphical Models
International Cross-Domain Conference on Machine Learning and Knowledge Extraction, 2019 One-digit multiplication problems is one of the major fields in learning mathematics at the level of primary school that has been studied over and over.
A. Saranti +3 more
semanticscholar +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model [PDF]
The Arabian journal for science and engineering, 2016 Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify
Anis Elbahi +3 more
semanticscholar +1 more source
Dynamic inference in probabilistic graphical models
, 2019 Probabilistic graphical models, such as Markov random fields (MRFs), are useful for describing high-dimensional distributions in terms of local dependence structures. The probabilistic inference is a fundamental problem related to graphical models, and sampling is a main approach for the problem. In this paper, we study probabilistic inference problems
Weiming Feng 0001 +3 more
openaire +4 more sources
Emerging experimental and computational methods for studying redox‐regulated structural transitions
FEBS Letters, EarlyView.Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass +2 more
wiley +1 more source
Algebraic discrete causal models [PDF]
, 2010 The main feature of the paper is to show that Algebraic Statistics is a natural framework to address issues of causality and to help discern a total cause.
Thwaites, Peter +2 more
core
Translophagy—A potential link between autophagy impairment and translational errors
FEBS Letters, EarlyView.Neurodegenerative diseases are characterised by the accumulation of abnormal proteins and protein aggregates, but their origin often remains unknown. We propose that selective autophagy removes damaged protein‐making machinery, preventing errors during protein synthesis.
Mykola V. Korolchuk +11 more
wiley +1 more source
GraphPPL.jl: A Probabilistic Programming Language for Graphical Models
EntropyThis paper presents GraphPPL.jl, a novel probabilistic programming language designed for graphical models. GraphPPL.jl uniquely represents probabilistic models as factor graphs.
Wouter W. L. Nuijten +2 more
doaj +1 more source