Results 211 to 220 of about 136,187 (283)
This study develops a deep learning‐based pathomics model to predict survival outcomes in pancreatic cancer patients. The CrossFormer architecture analyzes routine H&E‐stained tissue slides, identifying key prognostic features including stromal patterns, cellular characteristics, and immune infiltration.
Qiangda Chen +22 more
wiley +1 more source
SpaBalance: Balanced Learning for Efficient Spatial Multi-Omics Decoding. [PDF]
Cui Y +8 more
europepmc +1 more source
This study presents an impedance‐based single‐cell profiling platform that quantifies the electrical and mechanical properties of neutrophils across in vitro, in vivo, and clinical samples. The approach reveals distinct biophysical alterations associated with type 2 diabetes (T2DM) and cardiovascular complications, suggesting its potential utility for ...
Linwei He +13 more
wiley +1 more source
Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson's disease. [PDF]
Liu W, Xu L, Wang X, Wang J.
europepmc +1 more source
A comprehensive technology platform enables high‐fidelity, volumetric MALDI imaging of 3D cell cultures by integrating custom embedding molds, a semi‐automated computational framework for 3D reconstruction, voxel‐instead of pixel‐based biomarker discovery, and immersive mixed reality data exploration.
Stefania Alexandra Iakab +16 more
wiley +1 more source
Unraveling Stuttering Through a Multi-Omics Lens. [PDF]
Novaes Marques D.
europepmc +1 more source
The glycosyltransferase GALNT10 facilitates ovarian cancer metastasis through the induction of tumor cell EMT and tumor vascular dysfunction. GALNT10 enhanced the extracellular secretion of IGFBP7 through O‐GalNAc glycosylation modification at the T188 site, which subsequently interacts with CD93 on endothelial cells, leading to vascular remodeling ...
Yanan Zhang +9 more
wiley +1 more source
Ribosome plasticity in glioblastoma: a multi-omics framework for future investigation. [PDF]
Benelli D, Barbato C, Cogoni C.
europepmc +1 more source
S3RL: Enhancing Spatial Single‐Cell Transcriptomics With Separable Representation Learning
Separable Spatial Representation Learning (S3RL) is introduced to enhance the reconstruction of spatial transcriptomic landscapes by disentangling spatial structure and gene expression semantics. By integrating multimodal inputs with graph‐based representation learning and hyperspherical prototype modeling, S3RL enables high‐fidelity spatial domain ...
Laiyi Fu +6 more
wiley +1 more source

