Allergy & Immunology

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. While several biomarkers have been proposed, comprehensive immunological analyses integrating mass cytometry (CyTOF) and T-cell receptor sequencing (TCRseq) data remain limited. In this study, we applied the Latent Class Model based on the Bayesian Information Criterion (LCM-BIC) algorithm to integ...

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 cutting-edge cellular analysis tool by integrating high-resolution imaging technology, and has shown significant potential and application value in biomedical research. In this paper, we comprehensively review the evolution of IFC from its early theoretical development to its current mature application, ...

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effect...

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 has been shown by recent developments in microbiome...

Multi-omics analysis and experiments uncover the link between cancer intrinsic drivers, stemness, and immunotherapy in ovarian cancer with validation in a pan-cancer census.

BACKGROUND: Although immune checkpoint inhibitors (ICIs) represent a substantial breakthrough in cancer treatment, it is crucial to acknowledge that t...

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 outcome. Thus, identifying molecular predictors of...

Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The ...

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 elevated mortality rates. Exosomes, diminutive extracellu...

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 cell performance to create more powerful cancer imm...

Artificial Intelligence-Guided Identification of IGFBP7 as a Critical Indicator in Lactic Metabolism Determines Immunotherapy Response in Stomach Adenocarcinoma.

Due to considerable tumour heterogeneity, stomach adenocarcinoma (STAD) has a poor prognosis and varies in response to treatment, making it one of the...

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, identifying hub genes regulating mitophagy and de...

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 pathogenesis and poses a great threat to maternal and feta...

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 strategies for the effective therapy of cancer, as...

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 antigenic peptides, a process pivotal for cancer im...

A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area 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 form of B and T cell receptor sequences, but we ar...

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 form antibodies, and T cell receptors (TCRs)-is criti...

Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help fil...

Personalized and Safe Route Planning for Asthma Patients Using Real-Time Environmental Data

Asthmatic patients are very frequently affected by the quality of air, climatic conditions, and traffic density during outdoor activities. Most of t...

Precise Antigen-Antibody Structure Predictions Enhance Antibody Development with HelixFold-Multimer

The accurate prediction of antigen-antibody structures is essential for advancing immunology and therapeutic development, as it helps elucidate mole...