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Exploring the molecular link between arecoline exposure and prostate cancer-related alterations: integrative evidence from network toxicology, single-cell transcriptomics, molecular simulation, and experimental validation. [PDF]

Front Pharmacol
Zhu X, Liu H, Zeng Z, Zou L, Zhou Y, Zhou T.   +5 more
europepmc   +1 more source

Revealing the biomolecular response of glioma cells to helium, carbon and oxygen minibeam radiation therapy using synchrotron-based infrared microspectroscopy.

Analyst
González-Vegas R, Seksek O, Chiblak S, Brons S, Abdollahi A, Prezado Y, Yousef I, Martínez-Rovira I.   +7 more
europepmc   +1 more source

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The equivalence of two-dimensional PCA to line-based PCA

Pattern Recognition Letters, 2005
The state-of-the-art in human face recognition is the subspace methods originated by the Principal Component Analysis (PCA), the Eigenfaces of the facial images. Recently, a technique called Two-dimensional PCA (2DPCA) was proposed for human face representation and recognition.
Liwei Wang, Jufu Feng
exaly   +2 more sources

PCA Sparsified

SIAM Journal on Optimization, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fatih S. Aktas, Mustafa Ç. Pinar
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1D-PCA, 2D-PCA to nD-PCA

18th International Conference on Pattern Recognition (ICPR'06), 2006
In this paper, we first briefly reintroduce the 1D and 2D forms of the classical principal component analysis (PCA). Then, the PCA technique is further developed and extended to an arbitrary n-dimensional space. Analogous to 1D- and 2D-PCA, the new nD-PCA is applied directly to n-order tensors (n ges 3) rather than 1-order tensors (1D vectors) and 2 ...
Hongchuan Yu, Mohammed Bennamoun
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Performance Analysis of PCA, Sparse PCA, Kernel PCA and Incremental PCA Algorithms for Heart Failure Prediction

2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020
Heart failure (HF) prediction is a challenging issue in medical informatics and is considered a deadliest disease worldwide. Recent research has been concentrated on features transformation and selection for improved HF prediction. In this study, we search optimal feature extraction algorithm by evaluating the performance of different feature ...
Atiqur Rehman, Aurangzeb Khan, Muhammad Akhtar Ali, Muhammad Umair Khan, Shafqat Ullah Khan, Liaqat Ali   +5 more
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Two-Dimensional Quaternion PCA and Sparse PCA

IEEE Transactions on Neural Networks and Learning Systems, 2019
Benefited from quaternion representation that is able to encode the cross-channel correlation of color images, quaternion principle component analysis (QPCA) was proposed to extract features from color images while reducing the feature dimension. A quaternion covariance matrix (QCM) of input samples was constructed, and its eigenvectors were derived to
Xiaolin Xiao, Yicong Zhou
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Histogram PCA

2007
Histograms are data objects that are commonly used to characterize media objects like image, video, audio etc. Symbolic Data Analysis (SDA) is a field which deals with extracting knowledge and relationship from such complex data objects. The current research scenario of SDA has contributions related to dimensionality reduction of interval kind data ...
P. Nagabhushan, R. Pradeep Kumar
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PCA and kernel PCA

2014
Introduction Two primary techniques for dimension-reducing feature extraction are subspace projection and feature selection . This chapter will explore the key subspace projection approaches, i.e. PCA and KPCA. (i) Section 3.2 provides motivations for dimension reduction by pointing out (1) the potential adverse effect of large feature ...
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