Results 231 to 240 of about 110,596 (265)

FlowRoI: fast optical-flow-based Roi extraction for high-throughput immune cell image compression. [PDF]

open access: yesNpj Imaging
Xu X   +7 more
europepmc   +1 more source

PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction

open access: yesAdvanced Science, EarlyView.
PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Gangeun Park, Chaok Seok, Hahnbeom Park
wiley   +1 more source

Q‐LEAP: Millisecond Hyperdimensional Optimization for Full‐Spectrum Optical Metamaterials

open access: yesAdvanced Science, EarlyView.
Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Zikang Guo   +3 more
wiley   +1 more source

Transformer‐Based PET/CT Fusion Enables Preoperative Prediction and Prognostic Stratification of Spread Through Air Spaces in Lung Adenocarcinoma

open access: yesAdvanced Science, EarlyView.
An eight‐stream 2.5D Transformer integrates complementary PET metabolic and CT morphologic information to estimate spread‐through‐air‐spaces risk before surgery in lung adenocarcinoma. The model generalizes across independent centers, while its continuous risk score independently stratifies progression‐free survival, supporting further prospective ...
Xin‐Yu Zhu   +7 more
wiley   +1 more source

Diversity Beats Size Scaling for Chemical Language Models. [PDF]

open access: yesNPJ Drug Discov
Medina B   +4 more
europepmc   +1 more source

Flexible Integration of Structurally Diverse Spatial Multi‐Omics With AgaeSMO

open access: yesAdvanced Science, EarlyView.
AgaeSMO is a versatile and robust deep learning framework for flexible cross‐integration of spatial multi‐omics data. Leveraging a GATs network with a graph attention module, it learns joint embedding across diverse modalities, enabling integration of both structurally paired (e.g., spatial transcriptome‐epigenome, transcriptome‐proteome, transcriptome‐
Kunpeng Liu   +9 more
wiley   +1 more source

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