Results 51 to 60 of about 3,745,728 (280)
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Speech Emotion Recognition using Unsupervised Feature Selection Algorithms [PDF]
Radioengineering, 2020 The use of the combination of different speech features is a common practice to improve the accuracy of Speech Emotion Recognition (SER). Sometimes, this leads to an abrupt increase in the processing time and some of these features contribute less to ...
S. R. Bandela, T. K. Kumar
doaj
UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples ...
A. Kianisarkaleh +2 more
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BCG vaccination potentiates oxidative phosphorylation in neonatal myeloid‐derived suppressor cells
FEBS Open Bio, EarlyView.BCG vaccination enhances oxidative phosphorylation in neonatal MDSCs, impairing their immunosuppressive function. It upregulates electron transport chain genes and mitochondrial activity, increasing ATP and oxygen consumption. Pharmacological OXPHOS inhibition partially restores suppressive capacity, confirming causality.
Yingying Chen, Hui Li
wiley +1 more source
Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
Algorithms, 2020 Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems ...
Ibraheem Al-Jadir +3 more
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IET Image Processing, 2023 Unsupervised feature selection is a dimensionality reduction method and has been widely used as an important and indispensable preprocessing step in many tasks.
Lingli Guo, Xiuhong Chen
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Towards an Unsupervised Feature Selection Method for Effective Dynamic Features
IEEE Access, 2021 Dynamic features applications present new obstacles for the selection of streaming features. The dynamic features applications have various characteristics: a) features are processed sequentially while the number of instances is fixed; and b) the feature
Naif Almusallam +5 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Central nervous system (CNS) inflammatory demyelinating syndromes, including multiple sclerosis (MS), aquaporin‐4 antibody–positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD), and myelin oligodendrocyte glycoprotein (MOG) antibody–associated disease (MOGAD), occasionally overlap.
Bade Gulec +6 more
wiley +1 more source
Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection
IEEE Access, 2020 In this paper, we present a novel Local Sensitive Dual Concept Learning (LSDCL) method for the task of unsupervised feature selection. We first reconstruct the original data matrix by the proposed dual concept learning model, which inherits the merit of ...
Hua Zhao +3 more
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Unsupervised feature selection with structured graph optimization
, 2016 Since amounts of unlabelled and high-dimensional data needed to be processed, unsupervised feature selection has become an important and challenging problem in machine learning.
Zhu, Wei, Nie, Feiping, Li, Xuelong
core +1 more source