Results 61 to 70 of about 8,912,801 (239)
Molecular Oncology, EarlyView.This study identifies ARHGAP5, in addition to the frequently mutated ARHGAP35, as significantly mutated in endometrial cancer. Mutations in both genes co‐occur and are associated with their correlated downregulation. Functional CRISPR studies show that both paralogs regulate similar pathways, including actin cytoskeleton organization.
Mathilde Pinault +12 more
wiley +1 more source
Molecular Oncology, EarlyView.Incomplete microwave ablation (iMWA) of liver cancer triggers a biphasic progression in residual tumors. At Day 3, the microenvironment is characterized by acute inflammatory responses and extracellular matrix (ECM) remodeling. By Day 14, a profound shift occurs toward oncogenic signal transduction and immunosuppression, marked by macrophage ...
Yu Liu +9 more
wiley +1 more source
Differential Evolution Markov Chain with snooker updater and fewer chains [PDF]
, 2008 Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior.
Braak, C.J.F., ter, Vrugt, J.A.
core +1 more source
Molecular Oncology, EarlyView.Circulating microRNA profiles distinguish nonmalignant Lynch syndrome from cancer‐associated Lynch syndrome, sporadic colorectal cancer, and healthy controls. Age‐stratified analysis reveals disease‐specific regulatory states, and a six‐miRNA panel shows concordant separation in an independent colorectal tissue cohort.
Ramadhani Salum Chambuso +5 more
wiley +1 more source
Metalurgija, 2022 In order to improve the prediction accuracy of the rolling force Self-learning Model and change the phenomenon that the learning coefficient is unstable and the optimization process is not reasonable due to the experience value of the self-learning ...
X. L. Xi, B. Wang
doaj
Differential Evolution with Enhanced Abilities for Adaptation Applied to Heterogeneous Numerical Optimization [PDF]
, 2012 This article presents an exploration of Differential Evolution (DE) algorithm with enhanced adaptability. The main purpose of this study is to identify how this search method can cope with changes of the number of variables of a hard design test, unaided.
Lazarova, Mariya, Penev, Kalin
core +1 more source
RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells
FEBS Open Bio, EarlyView.RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour +2 more
wiley +1 more source
An area coverage algorithm for wireless sensor networks based on differential evolution
International Journal of Distributed Sensor Networks, 2018 Lifetime requirements and coverage demands are emphasized in wireless sensor networks. An area coverage algorithm based on differential evolution is developed in this study to obtain a given coverage ratio ε .
Ning-ning Qin, Jia-le Chen
doaj +1 more source
An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm
Applied Sciences, 2020 Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed
Yuanyuan Liu +4 more
doaj +1 more source
Differential Evolution for Multiobjective Portfolio Optimization [PDF]
Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk).
Thiemo Krink, Sandra Paterlini
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