Results 251 to 260 of about 1,673,769 (336)

His‐MMDM: Multi‐Domain and Multi‐Omics Translation of Histopathological Images with Diffusion Models

open access: yesAdvanced Science, EarlyView.
His‐MMDM is a diffusion model‐based framework for scalable multi‐domain and multi‐omics translation of histopathological images, enabling tasks from virtual staining, cross‐tumor knowledge transfer, and omics‐guided image editing. ABSTRACT Generative AI (GenAI) has advanced computational pathology through various image translation models.
Zhongxiao Li   +13 more
wiley   +1 more source

Association of spinal cord structure with cognition in hereditary spastic paraplegia type 5. [PDF]

open access: yesFront Neurol
Chen X   +9 more
europepmc   +1 more source

Inferring Gene Regulatory Networks From Single‐Cell RNA Sequencing Data by Dual‐Role Graph Contrastive Learning

open access: yesAdvanced Science, EarlyView.
RegGAIN is a novel and powerful deep learning framework for inferring gene regulatory networks (GRNs) from single‐cell RNA sequencing data. By integrating self‐supervised contrastive learning with dual‐role gene representations, it consistently outperforms existing methods in both accuracy and robustness.
Qiyuan Guan   +9 more
wiley   +1 more source

Time‐Efficient and Informatic‐Skill‐Light Gap‐Filling for Telomere‐to‐Telomere Genome Assembly

open access: yesAdvanced Science, EarlyView.
The paper introduces a novel auxiliary software toolbox GapSuite, consisting of two tools Gap‐Aid and Gap‐Graph, which guides users to fill gaps in chromosome‐level genome assembly using sequence‐extension‐based and assembly‐graph‐based strategies. The two tools enable users with limited informatics expertise to efficiently complete gap‐filling on ...
Dong Xu   +8 more
wiley   +1 more source

Generating Dynamic Structures Through Physics‐Based Sampling of Predicted Inter‐Residue Geometries

open access: yesAdvanced Science, EarlyView.
While static structure prediction has been revolutionized, modeling protein dynamics remains elusive. trRosettaX2‐Dynamics is presented to address this challenge. This framework leverages a Transformer‐based network to predict inter‐residue geometric constraints, guiding conformation generation via physics‐based iterative sampling. The resulting method
Chenxiao Xiang   +3 more
wiley   +1 more source

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