Results 51 to 60 of about 35,609 (308)

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

A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space-Time Adaptive Processing

Journal of Electrical and Computer Engineering, 2010
We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference ...
Yang Zhiwei, He Shun, Liao Guisheng, Ouyang Shan   +3 more
doaj   +1 more source

ProSiteHunter: A Unified Framework for Sequence‐Based Prediction of Protein‐Nucleic Acid and Protein‐Protein Binding Sites

Advanced Science, EarlyView.
This study proposed a unified sequence‐based framework for protein binding site prediction, which adopted a tri‐track semantic multi‐source feature fusion strategy to effectively capture diverse macromolecular interaction sites and further improved the accuracy of antibody‐antigen interaction prediction.
Dongliang Hou, Qihang Zhen, Zexin Lv, Xinyue Cui, Suhui Wang, Minghua Hou, Zhan Zhou, Xiaogen Zhou, Guijun Zhang   +8 more
wiley   +1 more source

Unsupervised Locality-Preserving Robust Latent Low-Rank Recovery-Based Subspace Clustering for Fault Diagnosis

IEEE Access, 2018
With the increasing demand for unsupervised learning for fault diagnosis, the subspace clustering has been considered as a promising technique enabling unsupervised fault diagnosis. Although various subspace clustering methods have been developed to deal
Jie Gao, Myeongsu Kang, Jing Tian, Lifeng Wu, Michael Pecht   +4 more
doaj   +1 more source

Multiple Kernel Subspace Clustering Based on Consensus Hilbert Space and Second-Order Neighbors

IEEE Access, 2020
How to deal with data sets in high-dimensional space is the focus of image processing. At present, subspace clustering method is one of the most commonly used methods for processing high-dimensional data sets. Traditional subspace clustering assumes that
Zhongyuan Wang, Jinglei Liu
doaj   +1 more source

Krylov subspace split Bregman methods [PDF]

, 2023
Split Bregman methods are popular iterative methods for the solution of large-scale minimization problems that arise in image restoration and basis pursuit. This paper investigates the possibility of projecting large-scale problems into a Krylov subspace
Alotaibi, Majed, Buccini, Alessandro, Reichel, Lothar   +2 more
core   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

SSA of biomedical signals: A linear invariant systems approach

, 2010
Singular spectrum analysis (SSA) is considered from a linear invariant systems perspective. In this terminology, the extracted components are considered as outputs of a linear invariant system which corresponds to finite impulse response (FIR) filters ...
Figueiredo, Nuno, Georgieva, P., Lang, E.W., Santos, I.M., Figueiredo, N., Teixeira, A.R., Tomé, A.M., Teixeira, Ana Rita   +7 more
core   +1 more source

Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates

Advanced Science, EarlyView.
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao, Yufei Liu, Ruixi Huang, Zhongxue Gan, Siyang Leng   +4 more
wiley   +1 more source

Matched direction detectors and estimators for array processing with subspace steering vector uncertainties [PDF]

, 2005
In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace ...
Besson, Olivier, Scharf, Louis, Vincent, François   +2 more
core