Latest AI and machine learning research in transplantation for healthcare professionals.
The function of a protein is intrinsically linked to its three-dimensional fold, and deep learning has revolutionized the field by enabling high-accuracy structure prediction at an unprecedented scale. Nevertheless, the growing deployment of these predictive pipelines in drug discovery and structural biology reveals a critical bottleneck that lies in the lack of independent and rigorous model accu...
Lung transplantation programs must decide when bilateral lung transplantation (BLT) offers meaningful functional benefit over single lung transplantation (SLT). Because donor and recipient characteristics jointly shape outcomes, the BLT-SLT contrast may differ across patients. However, observational registries pose a key statistical challenge: apparent subgroup differences can be artifacts of comp...
Engineering small-molecule binding proteins de novo remains a significant challenge as even advanced generative models struggle to model the atom-leve...
Proton therapy offers superior organ-at-risk sparing but is highly sensitive to anatomical changes, making accurate deformable image registration (DIR...
Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical varia...
Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medi...
The interaction between T cell receptors (TCRs), peptides, and human leukocyte antigens (HLAs) underlies antigen-specific T cell immunity. Despite sub...
Generative models can now propose thousands of \emph{de novo} antibody sequences, yet translating these designs into viable therapeutics remains const...
Cell state diversity drives tissue adaptability, repair, and disease resilience, but fully capturing this cellular complexity remains a challenge. Mos...
Deep learning has been widely applied to 3D medical image segmentation tasks. However, due to the diversity of imaging modalities, the high-dimensiona...
Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be t...
Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, ...
Collagen organisation within the tumour microenvironment plays a critical role in tumour progression and has emerged as an important structural biomar...
Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascul...
Background: Secure text messages (TMs) exchanged among interdisciplinary care teams in nursing homes (NHs) contain clinical information that aligns wi...
Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the...
Background: Interferon-gamma (IFN-{gamma}) is the primary effector cytokine of adaptive anti-tumor immunity, yet it paradoxically induces a potent imm...
Background: The advancement of single cell technologies has driven significant progress in constructing a multiscale, pan-organ Human Reference Atlas ...
Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scori...
Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards v...