Results 81 to 90 of about 26,519,848 (254)
Quantum Annealing for Robust Principal Component Analysis
IEEE Transactions on Quantum EngineeringPrincipal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the $L_{2}$-norm; however, the $L_{2 ...
Ian Tomeo +2 more
doaj +1 more source
FEBS Letters, EarlyView.Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley +1 more source
Principal Component Analysis of Crop Yield Response to Climate Change [PDF]
The objective of this study is to compare the effects of climate change on crop yields across different regions. A Principal Component Regression (PCR) model is developed to estimate the historical relationships between weather and crop yields for corn ...
Shurley, W. Donald +4 more
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3D reconstruction of coronal loops by the principal component analysis [PDF]
, 2013 Knowing the three dimensional structure of plasma filaments in the uppermost part of the solar atmosphere, known as coronal loops, and especially their length, is an important parameter in the wave-based diagnostics of this part of the Sun.
Nisticò, Giuseppe +6 more
core +1 more source
Augmented approach to desirability function based on principal component analysis
, 2022 The desirability function approach is commonly used in industry to tackle multiple response optimization problems. The shortcoming of this approach is that the variability and correlated in each predicted response are ignored.
Thorisingam, Yuvarani +1 more
core +1 more source
Modelling stem cell differentiation related processes—A practical overview for biologists
FEBS Letters, EarlyView.Stem cell differentiation is complex and difficult to control experimentally. This review introduces suitable computational modelling approaches that can support stem cell research, from mechanistic ODE and abstract models to multiscale and deep learning methods.
Ricco Zeegelaar +4 more
wiley +1 more source
Optimization of principal component analysis and k-nearest neighbors in cultivation area classification red onion [PDF]
This research aims to increase the effectiveness in classifying shallot cultivation areas through the combined application of principal component analysis (PCA) and k-nearest neighbors (KNN) methods.
Saidi Lubis, Fahdi; International Islamic University Malaysia +2 more
core +1 more source
An Eigenvalue test for spatial principal component analysis
BMC Bioinformatics, 2017 Background The spatial Principal Component Analysis (sPCA, Jombart (Heredity 101:92-103, 2008) is designed to investigate non-random spatial distributions of genetic variation.
V. Montano, T. Jombart
doaj +1 more source
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
The principal independent components of images [PDF]
, 2010 This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA).
Brause, Rüdiger W., Arlt, Björn
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