Allergy

Deep learning-based detection of eosinophilic esophagitis.

BACKGROUND: For eosinophilic esophagitis (EoE), a substantial diagnostic delay is still a clinically...

NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches.

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and d...

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressiv...

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pol...

Neural networks for increased accuracy of allergenic pollen monitoring.

Monitoring of airborne pollen concentrations provides an important source of information for the glo...

COVID-19 diagnosis by routine blood tests using machine learning.

Physicians taking care of patients with COVID-19 have described different changes in routine blood p...

Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning.

The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related t...

Deep Learning Methods for Improving Pollen Monitoring.

The risk of pollen-induced allergies can be determined and predicted based on data derived from poll...

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosi...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allerge...

Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology.

Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge....

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has ...

Applications of Machine and Deep Learning in Adaptive Immunity.

Adaptive immunity is mediated by lymphocyte B and T cells, which respectively express a vast and div...

Characterizing the function of domain linkers in regulating the dynamics of multi-domain fusion proteins by microsecond molecular dynamics simulations and artificial intelligence.

Multi-domain proteins are not only formed through natural evolution but can also be generated by rec...

Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging.

Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative me...

Deep learning approach to skin layers segmentation in inflammatory dermatoses.

Monitoring skin layers with medical imaging is critical to diagnosing and treating patients with chr...

Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy.

Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in H...

Automated severity scoring of atopic dermatitis patients by a deep neural network.

Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objecti...

Personalized prediction of early childhood asthma persistence: A machine learning approach.

Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience...

Accuracy of Algorithms to Identify People with Atopic Dermatitis in Ontario Routinely Collected Health Databases.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Becaus...