Results 231 to 240 of about 1,872,984 (299)

Integrated Multi‐Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC

open access: yesAdvanced Science, EarlyView.
Coexisting DCIS and IDC samples are profiled using spatial transcriptomics, single‐cell RNA sequencing, and single‐cell DNA sequencing. Integrative multi‐omics analysis reveals distinct malignant epithelial and microenvironmental features between DCIS and IDC, which are further validated using Xenium and multiplex immunohistochemistry.
Ning Zhang   +18 more
wiley   +1 more source

Generative AI‐Enabled Discovery and Experimental Demonstration of Ultra‐Broadband Epsilon‐Near‐Zero Perfect Absorbers

open access: yesAdvanced Science, EarlyView.
Researchers developed and applied a generative artificial intelligence (AI) model to optimize epsilon‐near‐zero thin film stacks for ultra‐broadband perfect absorption of light. The fabricated AI‐designed absorber, with a total thickness of only 160 nm, experimentally demonstrated an absorption bandwidth exceeding 1000 nm while maintaining greater than
David Dang   +10 more
wiley   +1 more source

Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery

open access: yesAdvanced Science, EarlyView.
DRIVE integrates PPI topology and pan‐cancer multi‐omics profiles through dual‐view graph disentanglement, contrastive representation learning, and joint optimization. Across six PPI networks, DRIVE outperforms ten baselines and remains robust to structural and annotation perturbations.
Xinjing Gong   +8 more
wiley   +1 more source

Adaptive Hyperparameter Optimization

open access: yes
This paper introduces an adaptive hyperparameter optimization algorithm designed to enhance model generalization. Traditional hyperparameter optimization methods often employ static strategies, failing to adapt to the intricacies of individual models.
openaire   +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

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