Significats de factorization en anglès

Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs.

Even more importantly, mathematicians are constantly developing new algorithms that allow for easier factorization.

These results are not compatible with the predictions based on the generalized factorization approach.

We extend the orbital-specific-virtual tensor factorization, introduced for local Møller-Plesset perturbation theory in Ref.

Our results are compared with previous measurements and with predictions based on Regge theory and factorization.

Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization.

The current paper 'introduces' a new method of array factorization that substantially accelerates linkage calculations with large numbers of markers.

Nonnegative matrix factorization (NMF) clustering of DNA copy number aberrations revealed three distinct molecular classes in this data set.

In this opinion article, we analyze the modest literature on applying tensor factorization to various biomedical fields including genotyping and phenotyping.

Dietary pesticide exposure profiles were identified using Non-Negative Matrix factorization (NMF), especially adapted for non-negative data with excess zeros.

Using nonnegative matrix factorization, we measured the contribution of each signature to carcinogenesis, and used hierarchical clustering to subtype each cohort.

This result is comparable in magnitude to corresponding ratios for W and dijet production but significantly lower than expectations based on factorization.

Among them, blind non-negative matrix factorization is gaining interest since it requires little assumptions about the spectra and concentration of the fluorochromes.

We performed class discovery using non-negative matrix factorization, and functional annotation using gene-set enrichment analyses, nearest template prediction, ingenuity pathway analyses, and immunohistochemistry.

To overcome this problem, we propose to make use of a modified Cholesky decomposition based on the indefinite factorization of local two-center Coulomb matrices.

Moreover, we show that these low dimensional representations favorably compare to descriptions obtained with more commonly used matrix factorization methods like PCA and ICA.