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Principal Component Analysis of Munich Functional Developmental Diagnosis
Pediatric Reports, 2021 Objectives: Munich Functional Developmental Diagnosis (MFDD) is a scale for assessing the psychomotor development of children in the first months or years of life.
Grażyna Pazera +3 more
doaj +1 more source
Parameterized principal component analysis [PDF]
Pattern Recognition, 2018 When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face.
Ajay Gupta, Adrian Barbu
openaire +2 more sources
FEBS Letters, EarlyView.Exposure to common noxious agents (1), including allergens, pollutants, and micro‐nanoplastics, can cause epithelial barrier damage (2) in our body's protective linings. This may trigger an immune response to our microbiome (3). The epithelial barrier theory explains how this process can lead to chronic noncommunicable diseases (4) affecting organs ...
Can Zeyneloglu +17 more
wiley +1 more source
Stable Analysis of Compressive Principal Component Pursuit
Algorithms, 2017 Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness.
Qingshan You, Qun Wan
doaj +1 more source
Manifold Regularized Principal Component Analysis Method Using L2,p-Norm
Mathematics, 2022 The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples.
Minghua Wan +3 more
doaj +1 more source
From omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan +2 more
wiley +1 more source
Principal Component Analysis in ECG Signal Processing
EURASIP Journal on Advances in Signal Processing, 2007 This paper reviews the current status of principal component analysis in the area of ECG signal processing. The fundamentals of PCA are briefly described and the relationship between PCA and Karhunen-Loève transform is explained.
Roig José Millet +4 more
doaj +2 more sources
N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
core
ERBIN limits epithelial cell plasticity via suppression of TGF‐β signaling
FEBS Letters, EarlyView.In breast and lung cancer patients, low ERBIN expression correlates with poor clinical outcomes. Here, we show that ERBIN inhibits TGF‐β‐induced epithelial‐to‐mesenchymal transition in NMuMG breast and A549 lung adenocarcinoma cell lines. ERBIN suppresses TGF‐β/SMAD signaling and reduces TGF‐β‐induced ERK phosphorylation.
Chao Li +3 more
wiley +1 more source
Principal Component Analysis on Recurrent Venous Thromboembolism
Clinical and Applied Thrombosis/Hemostasis, 2019 The rates of recurrent venous thromboembolism (RVTE) vary widely, and its causes still need to be elucidated. Statistical multivariate methods can be used to determine disease predictors and improve current methods for risk calculation.
Tiago D. Martins PhD +3 more
doaj +1 more source