Results 81 to 90 of about 2,420,035 (290)

The neural crest‐associated gene ERRFI1 is involved in melanoma progression and resistance toward targeted therapy

Molecular Oncology, EarlyView.
ERRFI1, a neural crest (NC)‐associated gene, was upregulated in melanoma and negatively correlated with the expression of melanocytic differentiation markers and the susceptibility of melanoma cells toward BRAF inhibitors (BRAFi). Knocking down ERRFI1 significantly increased the sensitivity of melanoma cells to BRAFi.
Nina Wang, Qian Sun, Daniel Novak, Lei Zhu, Juliane Poelchen, Tamara Steinfass, Yiman Wang, Viktor Umansky, Jochen Utikal   +8 more
wiley   +1 more source

On the secant method and the Ptak error estimates

Journal of Numerical Analysis and Approximation Theory, 1995
Not available.
Ioannis K. Argyros
doaj   +2 more sources

Optimal Error Estimates of Galerkin Finite Element Methods for Stochastic Partial Differential Equations with Multiplicative Noise

, 2011
We consider Galerkin finite element methods for semilinear stochastic partial differential equations (SPDEs) with multiplicative noise and Lipschitz continuous nonlinearities.
Kruse, Raphael
core   +1 more source

Transcriptional network analysis of PTEN‐protein‐deficient prostate tumors reveals robust stromal reprogramming and signs of senescent paracrine communication

Molecular Oncology, EarlyView.
Combining PTEN protein assessment and transcriptomic profiling of prostate tumors, we uncovered a network enriched in senescence and extracellular matrix (ECM) programs associated with PTEN loss and conserved in a mouse model. We show that PTEN‐deficient cells trigger paracrine remodeling of the surrounding stroma and this information could help ...
Ivana Rondon‐Lorefice, Jose I. Lopez, Aitziber Ugalde‐Olano, Maite Zufiaurre, Ianire Astobiza, Natalia Martin‐Martin, Laura Bozal‐Basterra, Saioa Garcia‐Longarte, Amaia Zabala‐Letona, Sofia Rey, Aida Santos‐Martin, Miguel Unda, Ana Loizaga‐Iriarte, Mariona Graupera, Paolo Nuciforo, Arkaitz Carracedo, Isabel Mendizabal   +16 more
wiley   +1 more source

Error Estimation

AbstractConsider first data-based machine learning techniques. They rely on large sets of examples provided during the training stage and do not learn with equations. Dealing with a situation that do not belong to the training set variability, namely an out-of-distribution sample, can be very challenging for these techniques.
David Ryckelynck, Fabien Casenave, Nissrine Akkari   +2 more
openaire   +1 more source

Least product relative error estimation

Journal of Multivariate Analysis, 2016
A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parameter.
Chen, Kani, Lin, Yuanyuan, Wang, Zhanfeng, Ying, Zhiliang   +3 more
openaire   +4 more sources

Cell surface interactome analysis identifies TSPAN4 as a negative regulator of PD‐L1 in melanoma

Molecular Oncology, EarlyView.
Using cell surface proximity biotinylation, we identified tetraspanin TSPAN4 within the PD‐L1 interactome of melanoma cells. TSPAN4 negatively regulates PD‐L1 expression and lateral mobility by limiting its interaction with CMTM6 and promoting PD‐L1 degradation.
Guus A. Franken, Andrea Abel Gutierrez, Imke van Rossum, Cornelia G. Spruijt, Michiel Vermeulen, Guido van Mierlo, Blanca Scheijen, Annemiek B. van Spriel   +7 more
wiley   +1 more source

Error estimates in atom coordinates and B factors in macromolecular crystallography. [PDF]

Curr Res Struct Biol, 2023
Helliwell JR.
europepmc   +1 more source

An investigation into the effectiveness of simulation-extrapolation for correcting measurement error-induced bias in multilevel models [PDF]

, 2015
Master's Project (M.S) University of Alaska Fairbanks, 2015This paper is an investigation into correcting the bias introduced by measurement errors into multilevel models. The proposed method for this correction is simulation-extrapolation (SIMEX).
Custer, Christopher
core  

Intermediate error estimates

Journal of Computational and Applied Mathematics, 1994
Let \(R[f]\) be the remainder of some linear approximation method (e.g. polynomial or spline approximation, numerical integration or differentiation), having estimates of the form \(| R[f ]|\leq \rho_ i\| f^{(i)} \|_{L^ p[a,b]}\), \(i=m,\dots, r\), for some \(m\) and \(r\) with \(0\leq m\leq r-1\). In many cases, \(\rho_ m\) and \(\rho_ r\), are known,
openaire   +2 more sources