Results 11 to 20 of about 1,727,048 (195)
Decoding Transdiagnostic Subtypes in Emotional Disorders. [PDF]
CNS Neurosci TherUsing nonnegative matrix factorization (NMF) clustering, our transdiagnostic study identified five representative functional networks (RFNs) that capture the heterogeneity of emotional disorders. These RFNs delineated five clinically distinct subtypes, each with unique diagnostic proportions, clinical symptomatology, and brain functional and structural
Wu Y +5 more
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Deep-Learning-Driven High-Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon-Limited Conditions. [PDF]
Adv Sci (Weinh)Highly overlapping fluorescent signals become distinguishable in photon‐limited living organisms via advanced imaging with intelligent reconstruction. The resulting in vivo hyperspectral imaging capability reveals nanoplastic uptake and circulation in live zebrafish, providing a new approach for studying complex biological and environmental processes ...
Li R +11 more
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Diagnosing Anaerobic Digesters' Function and Performance: From Meta-Omics to Integrated Meta-Omics Analyses. [PDF]
Environ Microbiol RepThe integration of multiple meta‐omics can tremendously improve our understanding of the structure, function and dynamics of the complex biological system in anaerobic digesters. Created in BioRender. https://BioRender.com/u9rj8ko. ABSTRACT Anaerobic digestion (AD) of organic wastes by microorganisms into biomethane contributes significantly towards ...
Ehiosun KI, Chapleur O, Mazéas L.
europepmc +2 more sources
Single‐Cell Multiomics Reveals That OTUD7B Is a Central Driver of Epithelial Remodelling and the Immunosuppressive Microenvironment in Pancreatic Ductal Adenocarcinoma [PDF]
J Cell Mol MedABSTRACT Pancreatic ductal adenocarcinoma (PAAD) is characterized by extreme malignancy and profound resistance to therapy, largely driven by a fibrotic and immunosuppressive tumour microenvironment (TME). The deubiquitinase OTU domain‐containing protein 7B (OTUD7B) has been implicated in inflammatory and oncogenic signalling, but its role in PAAD and ...
Liu X +4 more
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Generalized Separable Nonnegative Matrix Factorization Algorithm Based on Orthogonal Constraints [PDF]
Jisuanji gongcheng, 2023 Separable Nonnegative Matrix Factorization(NMF) is a special NMF method used to represent an entire dataset by extracting partial samples or key topics from the dataset.Generalized Separable Nonnegative Matrix Factorization(GSNMF) is an extended ...
Junhang CHEN, Zuyuan YANG, Mingyang LIU, Lingjiang LI
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Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing refers to a source separation problem of decomposing a hyperspectral imagery (HSI) to estimate endmembers, and their corresponding abundances.
Heng-Chao Li +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral spectral mixture analysis (SMA), which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been successfully used to tackle mixed-pixel problem in hyperspectral remote ...
Ge Zhang, Shaohui Mei, Yan Feng, Qian Du
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Wireless Communications and Mobile Computing, 2022 Nonnegative matrix factorization (NMF), which is aimed at making all elements of the factorization nonnegative and achieving nonlinear dimensional reduction at the same time, is an effective method for solving recommendation system problems. However, in many real‐world applications, most models learn recommendation models under the supervised learning ...
Ronghua Zhang +5 more
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Adaptive Kernel Graph Nonnegative Matrix Factorization
Information, 2023 Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized ...
Rui-Yu Li, Yu Guo, Bin Zhang
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Adaptive computation of the Symmetric Nonnegative Matrix Factorization (SymNMF)
SeMA Journal, 2020 Nonnegative Matrix Factorization (NMF), first proposed in 1994 for data analysis, has received successively much attention in a great variety of contexts such as data mining, text clustering, computer vision, bioinformatics, etc. In this paper the case of a symmetric matrix is considered and the symmetric nonnegative matrix factorization (SymNMF) is ...
P. Favati +3 more
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