Allergy & Immunology

Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

BACKGROUND: Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of these tools by using deidentified public health datasets for notifiable sexually transmitted infections (STIs) from a southern US county known for high...

Grounded GUI Understanding for Vision Based Spatial Intelligent Agent: Exemplified by Virtual Reality Apps

In recent years, spatial computing Virtual Reality (VR) has emerged as a transformative technology, offering users immersive and interactive experiences across diversified virtual environments. Users can interact with VR apps through interactable GUI elements (IGEs) on the stereoscopic three-dimensional (3D) graphical user interface (GUI). The accurate recognition of these IGEs is instrumental, ...

DANCE: Deep Learning-Assisted Analysis of Protein Sequences Using Chaos Enhanced Kaleidoscopic Images

Cancer is a complex disease characterized by uncontrolled cell growth. T cell receptors (TCRs), crucial proteins in the immune system, play a key ro...

Epidermal renewal during the treatment of atopic dermatitis lesions: A study coupling line-field confocal optical coherence tomography with artificial intelligence quantifications: LC-OCT reveals new biological markers of AD.

OBJECTIVE: This study explores the application of Line-field Confocal Optical Coherence Tomography (LC-OCT) imaging coupled with artificial intelligen...

Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions.

Monitoring enables timely action which is critical in avoiding asthma attacks. With the abundance of local weather and pollution data, when augmented ...

An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case ...

UnPaSt: unsupervised patient stratification by differentially expressed biclusters in omics data

Most complex diseases, including cancer and non-malignant diseases like asthma, have distinct molecular subtypes that require distinct clinical appr...

Predicting T-Cell Receptor Specificity

Researching the specificity of TCR contributes to the development of immunotherapy and provides new opportunities and strategies for personalized ca...

Examining the effectiveness of artificial intelligence applications in asthma and COPD outpatient support in terms of patient health and public cost: SWOT analysis.

This research aimed to examine the effectiveness of artificial intelligence applications in asthma and chronic obstructive pulmonary disease (COPD) ou...

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic option...

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 invasive adenocarcinoma (IAC) involves a complex evolutio...

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 accuracy is vital for personalised treatment approach...

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 heterogeneity, yet research on this aspect in cutaneous melan...

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

Nasal obstruction (NO), referring to blockage in the nasal cavity, is prevalent, affecting approximately one-third of the adult population. Consequent...

AI and immunology.

AI is rapidly becoming part of many aspects of daily life, with an impact that reaches all fields of research. We asked investigators to share their t...

The Immunopeptidomics Ontology (ImPO).

The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of s...

Immunocto: a massive immune cell database auto-generated for histopathology

With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on...

Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning.

In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral blood remains unclear. In this study, we established...

Improving Prediction of Survival and Progression in Metastatic Non-Small Cell Lung Cancer After Immunotherapy Through Machine Learning of Circulating Tumor DNA.

PURPOSE: To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) ...

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studi...