Zobrazeno 1 - 10
of 19
pro vyhledávání: '"David Degras"'
Autor:
Jingjing Lou, Yasaman Rezvani, Argenis Arriojas, Yihan Wu, Nachiket Shankar, David Degras, Caroline D. Keroack, Manoj T. Duraisingh, Kourosh Zarringhalam, Marc-Jan Gubbels
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-13 (2024)
Abstract Sequential lytic cycles driven by cascading transcriptional waves underlie pathogenesis in the apicomplexan parasite Toxoplasma gondii. This parasite’s unique division by internal budding, short cell cycle, and jumbled up classically defin
Externí odkaz:
https://doaj.org/article/a813754912e246219b7e973dd960c406
Publikováno v:
Genes, Vol 15, Iss 5, p 631 (2024)
Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and tran
Externí odkaz:
https://doaj.org/article/444fc4158d2945c484e6ade7c83c0fd6
Publikováno v:
Scientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
Abstract Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimen
Externí odkaz:
https://doaj.org/article/f44a5a3539404d7d921e917d3bb63b7d
Autor:
David Degras
Publikováno v:
Advances in Data Analysis and Classification. 15:625-671
This article introduces the sparse group fused lasso (SGFL) as a statistical framework for segmenting sparse regression models with multivariate time series. To compute solutions of the SGFL, a nonsmooth and nonseparable convex program, we develop a
Autor:
David Degras
This article is concerned with matching feature vectors in a one-to-one fashion across large collections of datasets. Formulating this task as a multidimensional assignment problem with decomposable costs (MDADC), we develop fast algorithms with time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25133db9411c3de93cdb28d80fcee8fd
Autor:
Hervé Cardot, David Degras
Publikováno v:
International Statistical Review. 86:29-50
Summary Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to perform the PCA of streaming dat
Publikováno v:
Scientific Reports
Scientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
Scientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
Discovery of robust diagnostic or prognostic biomarkers is a key to optimizing therapeutic benefit for select patient cohorts - an idea commonly referred to as precision medicine. Most discovery studies to derive such markers from high-dimensional tr
[Correction Notice: An Erratum for this article was reported in Vol 145(10) of Journal of Experimental Psychology: General (see record 2016-46925-004). In the article, there was an error in the Task, Stimuli, and Procedures section. In the 1st senten
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73fcfcb5fcf3f1cec6db0ce46310ea6a
https://europepmc.org/articles/PMC5585009/
https://europepmc.org/articles/PMC5585009/
Publikováno v:
IEEE Transactions on Signal Processing. 60:1087-1097
This paper considers the inference of trends in multiple, nonstationary time series. To test whether trends are parallel to each other, we use a parallelism index based on the L2 -distances between nonparametric trend estimators and their average. A
Autor:
David Degras
Publikováno v:
Statistics & Probability Letters. 78:2976-2980
We study the nonparametric estimation of the mean function of a random process indexed by a compact metric space. We elaborate on the asymptotic variance and prove asymptotic normality for a general class of linear estimators. An application to simul