Results 101 to 110 of about 4,208,641 (395)

Characteristics of the Kelch domain containing (KLHDC) subfamily and relationships with diseases

FEBS Letters, EarlyView.
The Kelch protein superfamily includes 63 members, with the KLHDC subfamily having 10 proteins. While their functions are not fully understood, recent advances in KLHDC2's structure and role in protein degradation have highlighted its potential for drug development, especially in PROTAC therapies.
Courtney Pilcher, Paula Armina V. Buco, Jia Q. Truong, Paul A. Ramsland, Monique F. Smeets, Carl R. Walkley, Jessica K. Holien   +6 more
wiley   +1 more source

A note on the prediction error of principal component regression [PDF]

arXiv, 2018
We analyse the prediction error of principal component regression (PCR) and prove non-asymptotic upper bounds for the corresponding squared risk. Under mild assumptions, we show that PCR performs as well as the oracle method obtained by replacing empirical principal components by their population counterparts.
arxiv  

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis

IEEE Transactions on Neural Networks and Learning Systems, 2018
Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes.
Xiaogang Deng, Xuemin Tian, Sheng Chen, C. Harris   +3 more
semanticscholar   +1 more source

The apo LETM1 F‐EF‐hand adopts a closed conformation that underlies a multi‐modal sensory role in mitochondria

FEBS Letters, EarlyView.
We present the first solution structure of the Ca2+‐depleted LETM1 F‐EF‐hand through a D676A/N678A Ca2+ binding‐deficient mutant, revealing a closed hydrophobic cleft caused by a unique F1‐helix pivot. The apo LETM1 F‐EF‐hand exhibits regiospecific hot and cold unfolding, sensitivity to physiological pH changes and potential for promiscuous heterotypic
Qi‐Tong Lin, Danielle M. Colussi, Peter B. Stathopulos   +2 more
wiley   +1 more source

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

Genome Biology, 2019
Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory.
Koki Tsuyuzaki, Hiroyuki Sato, Kenta Sato, I. Nikaido   +3 more
semanticscholar   +1 more source

Structural and mechanistic basis for the regulation of the chloroplast signal recognition particle by (p)ppGpp

FEBS Letters, EarlyView.
LHCPs are transported to the thylakoid membrane via the (cp)SRP pathway. This process involves a transit complex of (cp)SRP43, (cp)SRP54 and LHCP, which interacts with (cp)FtsY and Alb3 at the membrane. GTP hydrolysis by (cp)SRP54 and (cp)FtsY triggers complex dissociation.
Victor Zegarra, Paul Weiland, Pauline Anka Plitzko, Julia Thiery, Laura Czech, Felix Willmund, Patricia Bedrunka, Gert Bange   +7 more
wiley   +1 more source

History Matching Using Principal Component Analysis [PDF]

, 2011
Imperial Users ...
Sharma, Akshay, Sharma, Akshay
core  

Distinct dysregulated pathways in sporadic and Lynch syndrome‐associated colorectal cancer offer insights for targeted treatment

FEBS Letters, EarlyView.
This study explores the distinct molecular mechanisms underlying Lynch syndrome‐associated and sporadic colorectal cancer (CRC). By highlighting the therapeutic potential of targeting the PI3K‐Akt pathway in Lynch syndrome‐associated CRC and the Wnt pathway in sporadic CRC, the findings open avenues for personalised treatment strategies, aiming to ...
May J. Krause, Musalula Sinkala, Raj Ramesar   +2 more
wiley   +1 more source

Sparse Principal Component Analysis with missing observations [PDF]

arXiv, 2012
In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the high-dimensional setting with missing observations. Our goal is to estimate the first principal component when we only have access to partial observations. Existing estimation techniques are usually derived for fully observed data sets and require a prior knowledge ...
arxiv  

A Selective Overview of Sparse Principal Component Analysis

Proceedings of the IEEE, 2018
Principal component analysis (PCA) is a widely used technique for dimension reduction, data processing, and feature extraction. The three tasks are particularly useful and important in high-dimensional data analysis and statistical learning. However, the
H. Zou, Lingzhou Xue
semanticscholar   +1 more source