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
Meaningful Learning Model for Bible Learning
openaire +1 more source
This cross‐species study reveals that pathological hyperactivity of BNST neurons in depressive states disrupts inhibitory period and isolated spikes in the BNST‐NAc circuit. DBS achieves its antidepressant effects by precisely restoring network inhibitory periods and high‐fidelity signal transmission.
Xin Lv +12 more
wiley +1 more source
Beyond the hype: a psychological perspective on AI chatbots. [PDF]
Zhou X, Li C.
europepmc +1 more source
Information-Theoretic Intrinsic Motivation for Reinforcement Learning in Combinatorial Routing. [PDF]
Xi R, Ni Y, Wu W.
europepmc +1 more source
Finding value in the third year of cardiology fellowship in the new age of an accelerated pathway to electrophysiology. [PDF]
Haloot J.
europepmc +1 more source
"Better a one-dimensional image than no image at all" - an interview study on nursing educators' views on patient-perspective simulations. [PDF]
Steinacker AC +4 more
europepmc +1 more source
Deep learning in geographic atrophy: rethinking age-related macular degeneration progression and treatment. [PDF]
Shiromani S, Chhablani J.
europepmc +1 more source
Patients' needs and preferences in developing Art-Based Learning in outpatient palliative cancer care: A qualitative study. [PDF]
Geurts M +9 more
europepmc +1 more source

