Results 171 to 180 of about 3,495,066 (309)

Prediction of Adverse Post-Infarction Left Ventricular Remodeling Using a Multivariate Regression Model. [PDF]

Diagnostics (Basel), 2022
Oleynikov V, Salyamova L, Kvasova O, Burko N.   +3 more
europepmc   +1 more source

ZBTB11 Promotes Breast Cancer Progression by Activating FBXO28‐Mediated MST1 Degradation and Suppressing Hippo Signaling

Advanced Science, EarlyView.
ZBTB11 is identified as an oncogenic transcription factor that activates FBXO28 in breast cancer. FBXO28 promotes K48‐linked ubiquitination and degradation of MST1, suppressing Hippo signaling and enhancing epithelial–mesenchymal transition and metastasis. This transcription‐to‐ubiquitination cascade defines a prognostic biomarker axis and highlights a
An Xu, Xiang‐Nan Xu, Xiao Huang, Zhou Luo, Chun‐Lian Li, Yang Du, Cheng Yan, Xiao‐Jie Yu, Jian‐Wen Wang, Long‐Di Yao, De‐Yuan Fu   +10 more
wiley   +1 more source

Strictly monotone and smooth nonparametric regression for two or more variables [PDF]

In this article a new monotone nonparametric estimate for a regression function of two or more variables is proposed. The method starts with an unconstrained nonparametric regression estimate and uses successively one-dimensional isotonization procedures.
Dette, Holger, Scheder, Regine
core  

Penalized Estimation in Finite Mixtures of Multivariate Regression Models via the EM-PGM Algorithm. [PDF]

Stat Med
Kang H, Shin S.
europepmc   +1 more source

L2,1-norm regularized multivariate regression model with applications to genomic prediction. [PDF]

Bioinformatics, 2021
Mbebi AJ, Tong H, Nikoloski Z.
europepmc   +1 more source

Revolutionising Agricultural Sustainability: New ‘Furrow Tillage’ can Mitigate Short‐Term Soil‐to‐Atmosphere CO2 Flux and Promote Soil‐Plant‐Microbe Health

Advanced Science, EarlyView.
Furrow tillage resolves the conventional‐vs.‐no‐tillage trade‐off by simultaneously cutting CO2 efflux to 2.0–3.0 g C m−2 d−1 and unlocking high nutrient availability for the rice rhizosphere. This scalable agronomic solution strengthens soil health, enhances plant physiology, reshapes microbial metabolism, and shifts paddy systems toward a net ...
Arnab Majumdar, Munish Kumar Upadhyay, Argha Ghosh, Rakesh Biswas, Ioly Kotta Loizou, Martin Buck, Mark Tibbett, Biswajit Giri, Debojyoti Moulick, Manoj Kumar Jaiswal, Tarit Roychowdhury   +10 more
wiley   +1 more source

Multivariate Statistical Process Control Charts: An Overview [PDF]

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate ...
Bersimis, Sotiris, Panaretos, John, Psarakis, Stelios   +2 more
core  

B-MASTER: scalable Bayesian multivariate regression for master predictor discovery in colorectal cancer microbiome-metabolite profiles. [PDF]

Bioinformatics
Das P, Dey T, Peterson CB, Chakraborty S.   +3 more
europepmc   +1 more source

Molecular Dynamics‐Guided Sterol Engineering of mRNA‐Lipid Nanoparticles Reprograms Biodistribution and Enhances Spleen‐Selective Immunity

Advanced Science, EarlyView.
Sterol engineering enables precise control of mRNA lipid nanoparticles by tuning membrane organization. Replacing cholesterol with bile acid‐derived sterols redirects protein expression from the liver to the spleen. Notably, CA‐20 LNP enhances antigen‐specific immune responses while exhibiting a favorable safety profile, highlighting sterols as a ...
Sanghyuk Jeon, Seo‐Hyeon Bae, Jungyong Ji, Jisun Lee, Hosam Choi, Min‐Ho Kang, Sang‐In Park, Hyemin Kim, Nakyung Lee, Hajin Lee, Seonghoon Kim, Jungmin Kim, Subin Yoon, Seonghyun Lee, Seongje Cho, Dahyeon Ha, Ayoung Oh, Sohee Jo, Huijeong Choi, Yeeun Lee, Sowon Lee, Hyo‐Jung Park, Gitak Nam, Jisu Shin, Yujin Kang, Wonpil Im, Kiyoun Lee, Jae‐Hwan Nam   +27 more
wiley   +1 more source

A Note on the Dimension of the Projection Space in a Latent Factor Regression Model with Application to Business Cycle Classification [PDF]

In this paper it is shown that the number of latent factors in a multiple multivariate regression model need not be larger than the number of the response variables in order to achieve an optimal prediction.
Luebke, Karsten, Weihs, Claus
core