Results 31 to 40 of about 20,022 (282)
Modelling structured data with probabilistic graphical models [PDF]
, 2016 International audienceMost clustering and classification methods are based on the assumption that the objects to be clustered are independent. However, in more and more modern applications, data are structured in a way that makes this assumption not ...
F. Forbes, Forbes, Florence
core +1 more source
Bayesian graphical models for computational network biology
BMC Bioinformatics, 2018 Background Computational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules ...
Yang Ni, Peter Müller, Lin Wei, Yuan Ji
doaj +1 more source
From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source
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
Recent advances in imprecise-probabilistic graphical models
, 2012 We summarise and provide pointers to recent advances in inference and identification for specific types of probabilistic graphical models using imprecise probabilities.
De Bock, Jasper +2 more
core +2 more sources
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
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
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
JMIR Medical Informatics, 2020 BackgroundIt is important but challenging to understand the interactions of multiple chronic conditions (MCC) and how they develop over time in patients and populations.
Faruqui, Syed Hasib Akhter +7 more
doaj +1 more source
Molecular Oncology, EarlyView.In the present work, we have identified a transcriptional signature based on the differential expression of six genes (BCL2&MAST4, HSH2D&LAT2, METRN&PITPNM2) that would facilitate the early detection of T‐cell acute lymphoblastic leukemia (T‐ALL) patients prone to a poor treatment response and could be implemented at diagnosis, along with other risk ...
Antonio Lahera +11 more
wiley +1 more source