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PDGFC facilitates enzalutamide resistance in prostate cancer through activation of the Rap1-MAPK pathway. [PDF]

J Cancer Res Clin Oncol
Deng B, Chen S, Zhong D, Song G, Liu H, Xie L, Liu Y, Ni R, Shen W, Huang G.   +9 more
europepmc   +1 more source

The diagnostic value of prostate health index combined with soluble e-cadherin for prostate cancer. [PDF]

Front Endocrinol (Lausanne)
Ahamed Y, Hossain M, Baral S, Chen J, Sun W.   +4 more
europepmc   +1 more source

Portacaval anastomosis promotes fragmentation of mitochondrial network in the cerebellum of male rats. [PDF]

Metab Brain Dis
López-Cervantes M, Quintanar-Stephano A, Hérnandez-Pando R, Aguilar-Roblero R, Larriva-Sahd J, Vázquez-Martínez O, Martínez-Cabrera G, Díaz-Muñoz M.   +7 more
europepmc   +1 more source

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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.
Atiqur Rehman, Aurangzeb Khan, Muhammad Akhtar Ali, Muhammad Umair Khan, S. Khan, Liaqat Ali   +5 more
semanticscholar   +3 more sources

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.
Xiaolin Xiao, Yicong Zhou
semanticscholar   +4 more sources

Data-Driven Monitoring and Diagnosing of Abnormal Furnace Conditions in Blast Furnace Ironmaking: An Integrated PCA-ICA Method

IEEE transactions on industrial electronics (1982. Print), 2021
Principal component analysis (PCA) and independent component analysis (ICA) have been widely used for process monitoring in process industry. Since the operation data of blast furnace (BF) ironmaking process contain both non-Gaussian distribution data ...
P. Zhou, Ruiyao Zhang, J. Xie, Jiping Liu, Hong Wang, T. Chai   +5 more
semanticscholar   +1 more source

Spectral–Spatial and Superpixelwise PCA for Unsupervised Feature Extraction of Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2021
As the most classical unsupervised dimension reduction algorithm, principal component analysis (PCA) has been widely used in hyperspectral images (HSIs) preprocessing and analysis tasks. Recently proposed superpixelwise PCA (SuperPCA) has shown promising
Xin Zhang, Xinwei Jiang, Junjun Jiang, Yongshan Zhang, Xiaobo Liu, Z. Cai   +5 more
semanticscholar   +1 more source

PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification

IETE Technical Review, 2020
The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands.
Md. Palash Uddin, Md. Al Mamun, Md. Ali Hossain   +2 more
semanticscholar   +1 more source

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 ...
Mohammed Bennamoun, Hongchuan Yu
openaire   +2 more sources