Allergy & Immunology

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructiv...

Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns a...

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully suff...

Vibrational spectroscopy coupled with machine learning sheds light on the cellular effects induced by rationally designed TLR4 agonists.

In this work, we present the potential of Fourier transform infrared (FTIR) microspectroscopy to com...

The Application of mHealth and Artificial Intelligence to Chronic Rhinitis.

Immune Regulation Patterns in Response to Environmental Pollutant Chromate Exposure-Related Genetic Damage: A Cross-Sectional Study Applying Machine Learning Methods.

Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevatin...

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.

OBJECTIVE: Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, s...

The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications.

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond t...

Comprehensive analysis of lung adenocarcinoma: Unveiling differential gene expression, survival-linked genes, subtype stratification, and immune landscape implications.

This study offers a detailed exploration of lung adenocarcinoma (LUAD), addressing its heterogeneity...

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning.

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasi...

Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells.

T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer...

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine...

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and pat...

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promis...

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are ...

Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical...

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high he...

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few...

Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of ...

Machine learning identifies novel coagulation genes as diagnostic and immunological biomarkers in ischemic stroke.

BACKGROUND: Coagulation system is currently known associated with the development of ischemic stroke...

Patients' values and preferences for health states in allergic rhinitis-An artificial intelligence supported systematic review.

BACKGROUND: Allergic rhinitis (AR) impacts patients' physical and emotional well-being. Assessing pa...