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Principal Component Analysis

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Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

American journal of physiology. Heart and circulatory physiology
Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six cate...

Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference From fMRI Data.

IEEE transactions on bio-medical engineering
In this work, we conduct comprehensive comparisons between four variants of independent component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) methods, both at the subject-level, by using synthesized fMRI data with gr...

Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network.

Biomedical engineering online
BACKGROUND: With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis ...

Use of ultraviolet-visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index.

Environmental monitoring and assessment
The water quality index (WQI) is an important tool for water resource management and planning. However, it has major disadvantages: the generation of chemical waste, is costly, and time-consuming. In order to overcome these drawbacks, we propose to s...

Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.

Neural networks : the official journal of the International Neural Network Society
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve ab...

The Growing Curvilinear Component Analysis (GCCA) neural network.

Neural networks : the official journal of the International Neural Network Society
Big high dimensional data is becoming a challenging field of research. There exist a lot of techniques which infer information. However, because of the curse of dimensionality, a necessary step is the dimensionality reduction (DR) of the information....

Phylogenetic convolutional neural networks in metagenomics.

BMC bioinformatics
BACKGROUND: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architectu...

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Journal of neuroscience methods
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

Scientific reports
Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress...