Allergy & Immunology

Allergy

Latest AI and machine learning research in allergy for healthcare professionals.

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Autoencoder techniques for survival analysis on renal cell carcinoma.

Survival is the gold standard in oncology when determining the real impact of therapies in patients ...

Integrative Machine Learning of Glioma and Coronary Artery Disease Reveals Key Tumour Immunological Links.

It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing ...

Understanding TCR T cell knockout behavior using interpretable machine learning.

Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T c...

Automated Evaluation of Antibiotic Prescribing Guideline Concordance in Pediatric Sinusitis Clinical Notes.

BACKGROUND: Ensuring antibiotics are prescribed only when necessary is crucial for maintaining their...

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing a...

Predicting adaptive immune receptor specificities by machine learning is a data generation problem.

Determining the specificity of adaptive immune receptors-B cell receptors (BCRs), their secreted for...

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway w...

Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapie...

Unveiling Varied Cell Death Patterns in Lung Adenocarcinoma Prognosis and Immunotherapy Based on Single-Cell Analysis and Machine Learning.

Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a...

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM ...

Automated detection of maxillary sinus opacifications compatible with sinusitis from CT images.

BACKGROUND: Sinusitis is a commonly encountered clinical condition that imposes a considerable burde...

Grade classification of nasal obstruction from endoscopy videos using machine learning.

Nasal obstruction (NO), referring to blockage in the nasal cavity, is prevalent, affecting approxima...

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogenei...

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature.

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic acc...

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers.

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasiv...

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