Results 71 to 80 of about 24,126 (306)

Semantic Consistency Cross-Modal Retrieval With Semi-Supervised Graph Regularization

IEEE Access, 2020
Most of the existing cross-modal retrieval methods make use of labeled data to learn projection matrices for different modal data. These methods usually learn the original semantic space to bridge the heterogeneous gap, ignoring the rich semantic ...
Gongwen Xu, Xiaomei Li, Zhijun Zhang
doaj   +1 more source

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
doaj   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

Incorporating Multisource Knowledge To Predict Drug Synergy Based on Graph Co-regularization

, 2019
Drug combinations may reduce toxicity and increase therapeutic efficacy, offering a promising strategy to conquer multiple complex diseases. However, due to large-scale combinatorial space, it remains challenging to identify effective combinations ...
Jiawei Luo (763133), Zihan Lai (8273760), Guanghui Li (432992), Pingjian Ding (8273757), Cong Shen (840352), Cheng Liang (3939782)   +5 more
core   +1 more source

Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary

Remote Sensing
In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to ...
Xi Cheng, Ruiqi Mu, Sheng Lin, Min Zhang, Hai Wang   +4 more
doaj   +1 more source

Accounting for apparatus function when registering experimental data: peculiarity of choosing regularization parameter by L-curve criterion at deconvolution of the spectrum

Ядерна фізика та енергетика, 2019
Within the framework of the problem of spectrum deconvolution, variant of the choice of the regularization parameter by criterion of the L-curve, based on the displacement along the points of the L-curve graph, is proposed. An analysis of dependencies on
A. M. Sokolov
doaj   +1 more source

Clustering single-cell multimodal omics data with jrSiCKLSNMF

Frontiers in Genetics, 2023
Introduction: The development of multimodal single-cell omics methods has enabled the collection of data across different omics modalities from the same set of single cells. Each omics modality provides unique information about cell type and function, so
Dorothy Ellis, Arkaprava Roy, Susmita Datta   +2 more
doaj   +1 more source

Families of Regular Graphs in Regular Maps

Journal of Combinatorial Theory, Series B, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

Advanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source