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
Strategies to improve on selection based on estimated breeding values. [PDF]
Pook T +3 more
europepmc +1 more source
A biomimetic Prussian White nanoparticle (PW) is engineered to achieve long‐term local retention and orchestrate immunometabolic‐epigenetic remodeling for sciatic nerve regeneration. PW directly targets hexokinase 2 to suppress glycolysis, thereby elevating α‐ketoglutarate and driving Kdm4a/b‐mediated demethylation of H3K9me3.
Wenying Xu +6 more
wiley +1 more source
Nonlinear genomic selection index accelerates multi-trait crop improvement. [PDF]
Jesús Cerón-Rojas J +7 more
europepmc +1 more source
Polydopamine‐encapsulated probiotics restore gut homeostasis and reinstate macrophage efferocytosis, thereby reestablishing immune tolerance and mitigating systemic autoimmunity, highlighting a bioengineered microbiota‐based therapeutic strategy for SLE.
Ruimiao Wu +10 more
wiley +1 more source
Genomic selection using different marker types and densities [PDF]
A. K. Sonesson +15 more
core +1 more source
Genome selection for predicting the estimated breeding value of Canadian Holstein cattle
The objective of this study is to identify a subset of SNPs from a genome-wide dense panel of SNPs to predict the estimated breeding values (EBVs) of bulls. This requires selecting variables from a high-dimensional variable space (thousands of SNPs) with a much smaller sample size (hundreds of available animals).
openaire +1 more source
Optimizing progeny size and number of crosses under genomic selection: insights into additive and epistatic contributions to long-term genetic gain. [PDF]
da Silva Viana J +2 more
europepmc +1 more source
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu +10 more
wiley +1 more source
FSBLUP: a novel strategy of fusion similarity matrix construction via optimally integrating intermediate omics data to enhance genomic prediction. [PDF]
Xue Y +8 more
europepmc +1 more source

