Results 71 to 80 of about 1,020,025 (280)
Protein pyrophosphorylation by inositol pyrophosphates — detection, function, and regulation
FEBS Letters, EarlyView.Protein pyrophosphorylation is an unusual signaling mechanism that was discovered two decades ago. It can be driven by inositol pyrophosphate messengers and influences various cellular processes. Herein, we summarize the research progress and challenges of this field, covering pathways found to be regulated by this posttranslational modification as ...
Sarah Lampe +3 more
wiley +1 more source
Robust Bayesian functional principal component analysis
Statistics and ComputingWe develop a robust Bayesian functional principal component analysis (RB-FPCA) method that utilizes the skew elliptical class of distributions to model functional data, which are observed over a continuous domain. This approach effectively captures the primary sources of variation among curves, even in the presence of outliers, and provides a more ...
Zhang, Jiarui +2 more
openaire +3 more sources
Simplicial principal component analysis for density functions in Bayes spaces [PDF]
, 2016 Probability density functions are frequently used to characterize the distributional properties of large-scale database systems. As functional compositions, densities primarily carry relative information.
Filzmoser, P. +4 more
core +1 more source
Time after time – circadian clocks through the lens of oscillator theory
FEBS Letters, EarlyView.Oscillator theory bridges physics and circadian biology. Damped oscillators require external drivers, while limit cycles emerge from delayed feedback and nonlinearities. Coupling enables tissue‐level coherence, and entrainment aligns internal clocks with environmental cues.
Marta del Olmo +2 more
wiley +1 more source
IET Science, Measurement & Technology, 2022 This study is for the case where the available data of power transformer oil–paper insulation is limited to a small amount furfural data, to solve the problems in oil–paper insulation degradation modelling, such as few samples available, unknown function
Yuehan Qu +4 more
doaj +1 more source
Fast Generalized Functional Principal Components Analysis
, 2023 We propose a new fast generalized functional principal components analysis (fast-GFPCA) algorithm for dimension reduction of non-Gaussian functional data. The method consists of: (1) binning the data within the functional domain; (2) fitting local random intercept generalized linear mixed models in every bin to obtain the initial estimates of the ...
Leroux, Andrew +2 more
openaire +2 more sources
Sparse Functional Principal Component Analysis in High Dimensions
Statistica Sinica, 2023 27 pages, 2 figures, 3 ...
Hu, Xiaoyu, Yao, Fang
openaire +2 more sources
Functional Principal Component Analysis for Non-stationary Dynamic Time Series [PDF]
, 2018 Motivated by a highly dynamic hydrological high-frequency time series, we propose time-varying Functional Principal Component Analysis (FPCA) as a novel approach for the analysis of non-stationary Functional Time Series (FTS) in the frequency domain ...
Elayouty, Amira +3 more
core
Representation of Functional Data in Neural Networks [PDF]
, 2005 Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc.
Abraham +28 more
core +6 more sources
The newfound relationship between extrachromosomal DNAs and excised signal circles
FEBS Letters, EarlyView.Extrachromosomal DNAs (ecDNAs) contribute to the progression of many human cancers. In addition, circular DNA by‐products of V(D)J recombination, excised signal circles (ESCs), have roles in cancer progression but have largely been overlooked. In this Review, we explore the roles of ecDNAs and ESCs in cancer development, and highlight why these ...
Dylan Casey, Zeqian Gao, Joan Boyes
wiley +1 more source