Latest AI and machine learning research in prescriptions for healthcare professionals.
Many human diseases are polygenic conditions that arise from a complex interplay of interactions between multiple genes at different loci, but currently most Genome-Wide Association Studies (GWAS) largely only consider the main additive effects of single nucleotide polymorphisms (SNPs), resulting in a missing heritability problem in some complex traits. Identifying non-additive interactions, or ep...
Accurately predicting drug synergy is critical to accelerate the development of combination therapies for cancer and other complex diseases. Yet, the vast combinatorial drug and dose space poses a substantial challenge, even for modern deep learning approaches. Existing approaches often lack generalisability, collapse rich dose–response surfaces into single dose-averaged synergy scores, and fail t...
Resistance to targeted molecular therapies—both primary and acquired—remains a major obstacle to effective cancer treatment. Despite extensive researc...
Effective lung cancer detection from CT scans remains critically challenged by class imbalance where benign and normal cases are underrepresented, lea...
A quantitative measurement can have variation, referred to here as measurement variation, which is a probability distribution. Machine Learning models...
Targeted protein degradation (TPD) has transformed modern drug discovery by harnessing the ubiquitin–proteasome system to eliminate disease-driving pr...
Drug repurposing has become a crucial strategy to accelerate drug discovery and reduce development costs. Conventional drug development is time consum...
A comprehensive all-by-all receptor ligand affinity screen using Boltz-2, a deep learning framework for protein-ligand interaction prediction, reveals...
Agent systems powered by large language models (LLMs) are increasingly applied in computational biology to automate analysis, integrate data, and acce...
With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward for the pro...
Predicting antibacterial drug synergy remains difficult due to strain variability and the limited scale of experimentally tested combinations. Existin...
New systematic profiling of drug effects is in urgent demand due to limitations in existing drug assessment approaches to evaluate comprehensive drug ...
Recent advances in artificial intelligence have introduced deep learning and co-folding approaches for predicting protein-ligand complexes, raising th...
Accurate prediction of molecular-protein binding affinity (MPBA) is paramount in drug discovery, yet current computational models often lack generaliz...
Cell-free RNA (cfRNA) in human plasma provides a minimally invasive readout of tissue physiology, yet its extreme sparsity, heavy-tailed abundance dis...
Understanding the physicochemical principles governing intermolecular recognition remains a fundamental challenge across biochemistry, drug discovery,...
Accurate prediction of biomedical relationships, such as chemical–gene interactions, is fundamental to understanding disease mechanisms and advancing ...
Computational models of stress responses can highlight candidate genes underlying physiological adaptation, but their utility depends on rigorous vali...
Chronic diseases often require repeated oral or local administration, which can compromise patient compliance. In wet age-related macular degeneration...
Despite advances in central nervous system (CNS)-protective anesthetic and surgical strategies, perioperative stroke remains a significant concern in ...