Results 121 to 130 of about 418,262 (313)
Deep Probabilistic Graphical Modeling
CoRR, 2020 Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.
openaire +3 more sources
Molecular Oncology, EarlyView.Intratumour heterogeneity complicates precision management of advanced endometrial cancer. Circulating tumor DNA (ctDNA) offers a minimally invasive strategy to capture tumor evolution and therapeutic resistance. Here, we compare tumor‐agnostic NGS with tumor‐informed ddPCR, outlining their relative sensitivity, concordance, and clinical implications ...
Carlos Casas‐Arozamena +15 more
wiley +1 more source
Appendix A. Graphical model of an HMM for animal movement.
, 2016 Graphical model of an HMM for animal ...
Ruth King (2924808) +5 more
core +1 more source
Journal of Causal InferenceI thank Thomas Richardson and James Robins for their discussion of my article, and discuss the similarities and differences between their approach to causal modelling, based on single world intervention graphs, and my own decision-theoretic approach.
Dawid Philip
doaj +1 more source
Foresight of innovative processes in the environics of production systems
π-EconomyThe article reveals such urgent problems and areas of environmentalism as modern processes of interaction between production and the environment; increasing the sustainability of the environmental impact of production processes and innovative ways of ...
Kirilchuk Svetlana, Morozova Irina
doaj +1 more source
Molecular Oncology, EarlyView.Here, we demonstrate that HS1BP3 interacts with Cortactin through a proline‐rich region (PRR3.1) and show that this interaction, and HS1BP3 itself, promote cancer cell proliferation and invasion. Inhibition of this interaction leads to build‐up of TKS5 in multivesicular endosomes and altered secretion of CD63 and CD9, providing an explanation for the ...
Arja Arnesen Løchen +9 more
wiley +1 more source
A genetic algorithm for graphical model selection
AIC, Genetic Algorithm, Graphical model, Log-linear model, Model selection, Undirected graph,
Irene Poli, Alberto Roverato
core +1 more source
Causal Inference Using Graphical Models with the R Package pcalg
Journal of Statistical Software, 2012 The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data.
Markus Kalisch +4 more
doaj
Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source
Molecular Oncology, EarlyView.Dormant cancer cells can hide in distant organs for years, evading treatment and the immune system. This review highlights how signals from the surrounding tissue and immune environment keep these cells inactive or trigger their reawakening. Understanding these mechanisms may help develop therapies to eliminate or control dormant cells and prevent ...
Kanishka Tiwary +1 more
wiley +1 more source