Results 71 to 80 of about 1,131,696 (312)

Semi-Supervised Kernel PCA [PDF]

open access: yes, 2010
We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points.
Christian Walder   +4 more
core   +2 more sources

Heteroskedastic PCA: Algorithm, optimality, and applications [PDF]

open access: yesAnnals of Statistics, 2018
Principal component analysis (PCA) and singular value decomposition (SVD) are widely used in statistics, machine learning, and applied mathematics. It has been well studied in the case of homoskedastic noise, where the noise levels of the contamination ...
Anru R. Zhang, T. Cai, Yihong Wu
semanticscholar   +1 more source

LINC01116, a hypoxia‐lncRNA marker of pathological lymphangiogenesis and poor prognosis in lung adenocarcinoma

open access: yesMolecular Oncology, EarlyView.
The LINC01116 long noncoding RNA is induced by hypoxia and associated with poor prognosis and high recurrence rates in two cohorts of lung adenocarcinoma patients. Here, we demonstrate that besides its expression in cancer cells, LINC01116 is markedly expressed in lymphatic endothelial cells of the tumor stroma in which it participates in hypoxia ...
Marine Gautier‐Isola   +12 more
wiley   +1 more source

Mikrodalga Ön İşlemli Vakumlu Kurutma Yönteminin Portakal Dilimlerinin Renk ve Fenolik Bileşen Profili Üzerine Etkileri: Çok Değişkenli Analiz Yaklaşımı

open access: yesTurkish Journal of Agriculture: Food Science and Technology
Bu çalışmada, portakal dilimlerinin kalite özellikleri üzerine farklı sıcaklık (60, 70 ve 80°C) ve mutlak basınç (15 ve 30 kPa) kombinasyonlarında gerçekleştirilen vakumlu kurutma (VK) işlemlerinde mikrodalga ön işleminin (90 W, 30 dk) etkileri ...
Büşra Acoğlu Çelik   +3 more
doaj   +1 more source

Hybrid modeling and prediction of oyster norovirus outbreaks

open access: yesJournal of Water and Health, 2021
This paper presents a hybrid model for predicting oyster norovirus outbreaks by combining the Artificial Neural Networks (ANNs) and Principal Component Analysis (PCA) methods and using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite ...
Shima Shamkhali Chenar, Zhiqiang Deng
doaj   +1 more source

ATF4‐mediated stress response as a therapeutic vulnerability in chordoma

open access: yesMolecular Oncology, EarlyView.
We screened 5 chordoma cell lines against 100+ inhibitors of epigenetic and metabolic pathways and kinases and identified halofuginone, a tRNA synthetase inhibitor. Mechanistically halofuginone induces an integrated stress response, with eIF2alpha phosphorylation, activation of ATF4 and its target genes CHOP, ASNS, INHBE leading to cell death ...
Lucia Cottone   +11 more
wiley   +1 more source

Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model

open access: yesJOIN: Jurnal Online Informatika
Stroke is a disease which cause the death of brain cells, so that the part of the body controlled by the brain loses its function. If not treated immediately, this disease can cause long-term disability, brain damage, and death.
Untari Novia Wisesty   +3 more
doaj   +1 more source

Development and Evaluation of an Electronic Nose System Based on MOS Sensors to Detect and to Distinguish Lemon Essential Oils [PDF]

open access: yesJournal of Agricultural Machinery, 2019
Introduction Essences or essential oils are aromatic compounds that are found in different organs of the plants. Essences can be classified into three groups of natural, synthetic and natural like.
P Fayyaz   +3 more
doaj   +1 more source

Optimizing Face Recognition Using PCA

open access: yes, 2012
Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially
Abdullah, Manal   +2 more
core   +1 more source

On the Applications of Robust PCA in Image and Video Processing

open access: yesProceedings of the IEEE, 2018
Robust principal component analysis (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing, and 3-D computer vision.
T. Bouwmans   +4 more
semanticscholar   +1 more source

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