Results 191 to 200 of about 541,400 (289)

Unveiling patterns in spatial transcriptomics data: a novel approach utilizing graph attention autoencoder and multiscale deep subspace clustering network. [PDF]

Gigascience
Zhou L, Peng X, Chen M, He X, Tian G, Yang J, Peng L.   +6 more
europepmc   +1 more source

Regularized Multi-View Subspace Clustering for Common Modules Across Cancer Stages. [PDF]

Molecules, 2018
Zhang E, Ma X.
europepmc   +1 more source

PARSEC.py: A Python‐Based Real‐Space Kohn–Sham Density Functional Theory Code Accelerated by Machine Learned Charge Density

Journal of Computational Chemistry, Volume 47, Issue 23, September 5, 2026.
PARSEC.py is a Python‐based real‐space Kohn–Sham DFT framework that leverages the Python scientific ecosystem for transparent, modular integration of machine‐learned densities and GPU acceleration. This unified design enables more efficient and scalable first‐principles simulations of large chemical and materials systems. ABSTRACT PARSEC.py is a Python‐
Zeyi Zhang, Carlos Mora Perez, Patrick Kwon, Martin Head‐Gordon, Jin Qian   +4 more
wiley   +1 more source

Application of Subspace Clustering in DNA Sequence Analysis. [PDF]

J Comput Biol, 2015
Wallace T, Sekmen A, Wang X.
europepmc   +1 more source

Subspace clustering for complex data.

, 2012
The increasing potential of storage technologies and information systems has opened the possibility to conveniently and affordably gather large amounts of complex data. Going beyond simple descriptions of objects by some few characteristics, such data sources range from high dimensional vector spaces over imperfect data containing errors to network ...
openaire   +3 more sources

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

Advanced Science, Volume 13, Issue 51, 14 September 2026.
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

LogDet Rank Minimization with Application to Subspace Clustering. [PDF]

Comput Intell Neurosci, 2015
Kang Z, Peng C, Cheng J, Cheng Q.
europepmc   +1 more source

Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering

Advanced Intelligent Systems, Volume 8, Issue 9, September 2026.
Robust feature discovery in incomplete multi‐view clustering is achieved by coupling RPCA‐based clean representation recovery with neural‐network‐assisted graph learning. The resulting RIMVC framework constructs cleaner and more discriminative graph‐structured representations from incomplete and noisy multi‐view data, improving clustering robustness ...
Ping Hu, Chenggang Lu, Rui Zhang, Haijun Yan, Yesong Xu   +4 more
wiley   +1 more source

Robust auto-weighted multi-view subspace clustering with common subspace representation matrix. [PDF]

PLoS One, 2017
Zhuge W, Hou C, Jiao Y, Yue J, Tao H, Yi D.   +5 more
europepmc   +1 more source

Quantum Limits to Linewidth Narrowing in Single‐ and Few‐Atom Cavity Electromagnetically Induced Transparency

Annalen der Physik, Volume 538, Issue 9, September 2026.
Electromagnetically induced transparency can narrow the transmission resonance of a cavity using a control field. However, this narrowing has a quantum limit: an optimal control‐field strength yields the minimum achievable linewidth. This limit is determined not only by the control field, but also by the probe amplitude, the number of atoms, and their ...
Lucas R. S. Santos, Murilo H. Oliveira, Luiz O. R. Solak, Daniel Z. Rossatto, Celso J. Villas‐Boas   +4 more
wiley   +1 more source