Results 151 to 160 of about 12,744 (261)

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

open access: yesAdvanced 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   +8 more
wiley   +1 more source

Robust weakly supervised learning for COVID-19 recognition using multi-center CT images. [PDF]

open access: yesAppl Soft Comput, 2022
Ye Q   +10 more
europepmc   +1 more source

Phase‐Resolved Defect Transport Mechanisms Governing Asynchronous Ordering in a Eutectic High‐Entropy Alloy

open access: yesAdvanced Science, EarlyView.
Phase‐resolved experiments and atomistic simulations reveal asynchronous ordering behaviors in a eutectic high‐entropy alloy during isothermal annealing. Distinct defect transport mechanisms are identified in coexisting B2 and BCC phases, showing that vacancy and interstitial mediated diffusion governs phase‐dependent thermal stability.
Huiwen Yao   +5 more
wiley   +1 more source

Early detection of metastatic risk in primary cutaneous melanoma using weakly supervised learning. [PDF]

open access: yesSci Rep
Dahlén F   +7 more
europepmc   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

open access: yesAdvanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen   +6 more
wiley   +1 more source

Special issue on weakly supervised learning

open access: yesJournal of Visual Communication and Image Representation, 2016
Zhang, L.   +4 more
openaire   +3 more sources

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