Allergy & Immunology

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

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Showing 1282-1302 of 5,423 articles
Integration of T cell repertoire, CyTOF, genotyping and symptomatology data reveals subphenotypic variability in COVID-19 patients.

COVID-19 manifests a broad spectrum of clinical outcomes, from asymptomatic cases to severe disease....

Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a common cancer associated with elevate...

Identification of potential diagnostic markers and molecular mechanisms of asthma and ulcerative colitis based on bioinformatics and machine learning.

BACKGROUNDS: Asthma and ulcerative colitis (UC) are chronic inflammatory diseases linked through the...

Home spirometry telemonitoring in pediatric patients with asthma: a mixed study.

BACKGROUND: To evaluate the feasibility and practicality of home spirometry telemonitoring for pedia...

HIV multidrug class resistance prediction with a time sliding anchor approach.

MOTIVATION: The emergence of multidrug class resistance (MDR) in Human Immunodeficiency Virus (HIV) ...

Imaging flow cytometry: from high - resolution morphological imaging to innovation in high - throughput multidimensional biomedical analysis.

Imaging flow cytometry (IFC), as an extension of conventional flow cytometry, has emerged as a cutti...

Microbiota as diagnostic biomarkers: advancing early cancer detection and personalized therapeutic approaches through microbiome profiling.

The important function of microbiota as therapeutic modulators and diagnostic biomarkers in cancer h...

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 ...

Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning.

Preeclampsia (PE) is a pregnancy-specific disorder characterized by an unclearly understood pathogen...

Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.

Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However,...

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...

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...

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning.

The adaptive immune system holds invaluable information on past and present immune responses in the ...

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...

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