Results 221 to 230 of about 601,312 (265)

CEACAM1 participation in breast cancer progression

Molecular Oncology, EarlyView.
In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin, Irina Kube‐Golovin, Anatolii Romaniuk, Gunther Wennemuth   +3 more
wiley   +1 more source

Pharmacological chromatin remodeling enhances response to estrogen therapy in ER+ breast cancer

Molecular Oncology, EarlyView.
Estrogen therapy elicits clinical benefit in ~ 30% of patients with endocrine‐resistant estrogen receptor (ER)‐positive breast cancer. Based on findings that ER transcriptional activation underlies response to estrogen therapy, we tested the effects of epigenetic dysregulation via pharmacological inhibition of histone deacetylases (HDACi).
Anneka L. Johnson Thomas, Barbara Karakyriakou, Patricia C. Muskus, Alyssa M. Roberts, Justin Rendleman, Alexa Warren, Hanxu Lu, Kimberly A. Birmingham, Malone K. Friedman, Abigail E. Goen, Huijuan Yang, Owen M. Wilkins, Fred W. Kolling, Eugene Demidenko, Julie M. Jorns, Brock C. Christensen, Todd W. Miller   +16 more
wiley   +1 more source

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Principal components of mania

Journal of Affective Disorders, 2003
An alternative to the categorical classification of psychiatric diseases is the dimensional study of the signs and symptoms of psychiatric syndromes. To date, there have been few reports about the dimensions of mania, and the existence of a depressive dimension in mania remains controversial.
A, González-Pinto, J, Ballesteros, A, Aldama, J L, Pérez de Heredia, M, Gutierrez, F, Mosquera, A, González-Pinto   +6 more
openaire   +2 more sources

Stability of principal components

Computational Statistics, 2007
In this article we deal with the problem of stability of the conclusions from principal components analysis over repeated samples. We define a measure of stability for each component and investigate some of the measures properties. We then obtain the maximum likelihood estimators (MLEs) of the measures, and derive their joint limiting distributions ...
A. H. Al-Ibrahim, Noriah M. Al-Kandari
openaire   +1 more source

Principal Components Analysis

2012
Principal components analysis (PCA) is a standard tool in multivariate data analysis to reduce the number of dimensions, while retaining as much as possible of the data's variation. Instead of investigating thousands of original variables, the first few components containing the majority of the data's variation are explored.
Groth, D., Hartmann, S., Klie, S., Selbig, J.   +3 more
openaire   +3 more sources

PRINCIPAL COMPONENT VALUE AT RISK

International Journal of Theoretical and Applied Finance, 2000
Value at risk (VaR) is an industrial standard for monitoring financial risk in an investment portfolio. It measures potential losses within a given confidence interval. The implementation, calculation, and interpretation of VaR contains a wealth of mathematical issues that are not fully understood.
Brummelhuis, R., Córdoba, A., Quintanilla, M., Seco, L.   +3 more
openaire   +2 more sources

Nonlinear principal component analysis to preserve the order of principal components

Neurocomputing, 2004
Principal component analysis (PCA) is an effective method of linear dimensional reduction. Because of its simplicity in theory and implementation, it is often used for analyses in various disciplines. However, because of its linearity, PCA is not always suitable, and has redundancy in expressing data.
Ryo Saegusa, Hitoshi Sakano, Shuji Hashimoto   +2 more
openaire   +1 more source

Principal Components and Principal Clusters

Journal of Information and Optimization Sciences, 1987
Abstract The use of principal components to reduce the number of dimensions so that graphieal representation of the data is possible has been developed. One. of the most important applications is the connexion with cluster analysis. It has not been defined the criteria by which to decide whether there is any justification for dividing a set of ...
Haruo Miyazaki, Youichi Seki
openaire   +1 more source

The efficient cross-validation of principal components applied to principal component regression

Statistics and Computing, 1995
The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of omitting each observation in turn and repeating the principal component calculations is computationally costly. In this paper we present an efficient approach to leave-one-out cross-validation of principal components.
Bart J. A. Mertens, Tom Fearn, Michael Thompson   +2 more
openaire   +1 more source

Coupled Principal Component Analysis

IEEE Transactions on Neural Networks, 2004
A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the
Möller, Ralf, Könies, Axel
openaire   +5 more sources