Integrating Spatial Proteogenomics in Cancer Research
Advanced Science, EarlyView.Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang +13 more
wiley +1 more source
A generalizable 3D framework and model for self-supervised learning in medical imaging. [PDF]
NPJ Digit MedXu T +6 more
europepmc +1 more source
Machine Learning‐Guided Engineering of Protein Phase Separation Properties in Immune Regulation
Advanced Science, EarlyView.PScalpel, a machine learning model integrating protein structure extraction, graph contrastive learning, and a genetic algorithm, guides the engineering of protein phase separation ability. It adopts transfer learning methods to provide predictive recommendations for protein phase separation ability changes through single amino acid mutations in a ...
Chenqiu Zhang +9 more
wiley +1 more source
End-to-end non-invasive ECG signal generation from PPG signal: a self-supervised learning approach. [PDF]
Front PhysiolYalcin M, Latoschik ME.
europepmc +1 more source
Advancing Precision Nutrition Through Multimodal Data and Artificial Intelligence
Advanced Science, EarlyView.Individual responses to food vary dramatically, challenging traditional dietary advice. This review explores how the unique genetic makeup, gut microbiome, and brain activity shape host metabolic health. We examine how artificial intelligence integrates these multimodal data to predict individualized dietary needs, moving beyond one‐size‐fits‐all ...
Yuanqing Fu +5 more
wiley +1 more source
Weakly supervised colorectal gland segmentation through self-supervised learning and attention-based pseudo-labeling. [PDF]
Sci RepWen H, Wu Y, Huang D, Liu C.
europepmc +1 more source
Advanced Science, EarlyView.BiSCALE: A pathology‐driven deep learning framework for multi‐scale gene expression prediction from whole‐slide images. It accurately infers bulk and near‐cellular spot‐level expression, links predictions to clinical phenotypes, identifies disease‐associated niches, and enables applications in risk stratification and cell‐identity annotation, providing
Hailong Zheng +8 more
wiley +1 more source
Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology. [PDF]
JHEP RepBeaufrère A +16 more
europepmc +1 more source
Subspace Clustering on Incomplete Data with Self-Supervised Contrastive Learning [PDF]
Huanran Li, Daniel Pimentel-Alarcón
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