Results 171 to 180 of about 24,713,244 (334)
A single‐cell atlas of pancreatic ductal adenocarcinoma development reveals progressive ductal‐fibroblast‐immune crosstalk. Tumor‐derived LAMB3 drives the formation of immunosuppressive LRRC15+ fibroblasts through the ITGB1/FAK/MAPK/FOSL2 signaling. Glycolytic reprogramming upregulates LAMB3 and correlates with LRRC15+ fibroblast enrichment.
Xuqing Shi +23 more
wiley +1 more source
Endothelial GPR68 is identified as a critical regulator of collateral artery growth in peripheral artery disease. Genetic and pharmacological evidence demonstrates that GPR68 integrates hemodynamic cues to drive monocyte recruitment and inflammatory remodeling, thereby promoting collateral arteriogenesis and tissue perfusion after ischemia ...
Yiyan Song +12 more
wiley +1 more source
missForestPredict-Missing data imputation for prediction settings. [PDF]
Albu E, Gao S, Wynants L, Van Calster B.
europepmc +1 more source
Integrating Spatial Proteogenomics in Cancer Research
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
SGA-DT: An adaptive fusion framework for missing data imputation and interpretable healthcare classification. [PDF]
Jena M, Dehuri S, Cho SB.
europepmc +1 more source
A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application
This review presents a strategy‐centric framework for spatial omics technologies, organizing methods by how spatial information is experimentally encoded. It compares key performance trade‐offs across sequencing‐ and imaging‐based approaches, examines computational and practical limitations, and highlights biomedical applications. The analysis provides
Xiaofeng Wu +5 more
wiley +1 more source
Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility. [PDF]
Moallemian S +7 more
europepmc +1 more source
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
Proteomics Data Imputation With a Deep Model That Learns From Many Datasets. [PDF]
Harris L, Noble WS.
europepmc +1 more source
Multi‐omic profiling of T1 high‐grade bladder cancer identifies a high‐risk subtype (T1HG1) driven by NQO1, which couples anoikis resistance with immune evasion. NQO1 orchestrates macrophage–T cell crosstalk suppression via CXCL9 modulation. Pharmacological NQO1 inhibition with skullcapflavone II enhances cisplatin efficacy, representing a promising ...
Bin Guo +20 more
wiley +1 more source

