Results 181 to 190 of about 3,040,602 (285)

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi   +7 more
wiley   +1 more source

BraMARS: An Interpretable Histopathology‐Driven Deep Learning Model for Brain Metastasis Risk Stratification in Surgically Resected Limited‐Stage SCLC

open access: yesAdvanced Science, EarlyView.
This study presents BraMARS, an explainable deep learning model that estimates future brain metastasis risk in surgically resected limited‐stage small‐cell lung cancer using routine H&E‐stained whole‐slide images. By linking model‐attributed spatial histopathology with clinical outcomes and proteomic programs, BraMARS provides a biologically ...
Zijian Yang   +10 more
wiley   +1 more source

Full dynamic similarity experiments and predictive modeling of wind turbine aerodynamics across control strategies. [PDF]

open access: yesPNAS Nexus
Kurelek JW   +6 more
europepmc   +1 more source

Dipolar Coupling Errors in Nuclear Spin Qubits

open access: yesAdvanced Science, EarlyView.
Multi‐nuclear spin registers of phosphorus‐atom qubits in silicon have demonstrated 99.99% single and >$>$99.9% two qubit gate fidelities. Despite nanometer separation, read‐out fidelities between individual nuclear spins within the register are not identical.
I. D. Thorvaldson   +15 more
wiley   +1 more source

Computationally Evidence‐Grounded Sequence‐First Design of Peptide Binders

open access: yesAdvanced Science, EarlyView.
BOND‐PEP enables controllable, sequence‐first peptide binder design by grounding generation in binding evidence retrieved for each target. It uses topology‐conditioned message passing to integrate relevant peptide examples with the target protein sequence, forming a residue‐level representation that guides the generation of diverse, target‐specific ...
Wenze Ding
wiley   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

open access: yesAdvanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

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