Latest AI and machine learning research in allergy for healthcare professionals.
BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their...
Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T c...
Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and var...
It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing ...
MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing a...
Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted for...
Clinical trials are an indispensable part of the drug development process, bridging the gap betwee...
In the evolving domain of Human Activity Recognition (HAR) using Internet of Things (IoT) devices,...
This paper reports on a study exploring user experiences with suspicious emails and associated war...
Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway w...
Purpose: Analyzing noninvasive longitudinal and multimodal data using artificial intelligence coul...
Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical b...
Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of ...
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapie...
We propose a novel approach to effectively detect cloned identities of social-sensor cloud service...
BACKGROUND: Sinusitis is a commonly encountered clinical condition that imposes a considerable burde...
The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM ...
Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a...
Biologists frequently desire protein inhibitors for a variety of reasons, including use as researc...
This study introduces a compositional autoencoder (CAE) framework designed to disentangle the comp...
This paper tackles the challenge of running multiple ML inference jobs (models) under time-varying...