Allergy & Immunology

Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC.

BACKGROUND: Immunotherapy has introduced new breakthroughs in improving the survival of head and neck squamous cell carcinoma (HNSCC) patients, yet drug resistance remains a critical challenge. Developing personalized treatment strategies based on the molecular heterogeneity of HNSCC is essential to enhance therapeutic efficacy and prognosis.

Assessing the diagnostic accuracy of machine learning algorithms for identification of asthma in United States adults based on NHANES dataset.

Asthma diagnosis poses challenges due to underreporting of symptoms, misdiagnoses, and limitations in existing diagnostic tests. Machine learning (ML) offers a promising avenue for addressing these challenges by leveraging demographic and clinical data. In this study, we aim to compare different ML diagnostic models and obtain the most valuable features for asthma diagnosis using data from the Nat...

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using gen...

Two-stage CNN-based framework for leukocytes classification.

Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide path...

Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microen...

In-silico exploring pathway and mechanism-based therapeutics for allergic rhinitis: Network pharmacology, molecular docking, ADMET, quantum chemistry and machine learning based QSAR approaches.

Allergic rhinitis is a devastating health complication that interrupts the quality of daily life and significantly affects around 40 % of the populati...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging ou...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the forme...

An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.

Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagno...

Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning.

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigat...

Simultaneous Isotropic Omnidirectional Hypersensitive Strain Sensing and Deep Learning-Assisted Direction Recognition in a Biomimetic Stretchable Device.

Omnidirectional strain sensing and direction recognition ability are features of the human tactile sense, essential to address the intricate and dynam...

Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer.

Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the imm...

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiom...

Elucidating the Mechanism of VVTT Infection Through Machine Learning and Transcriptome Analysis.

The vaccinia virus (VV) is extensively utilized as a vaccine vector in the treatment of various infectious diseases, cardiovascular diseases, immunode...

Concentration-dependent effects of immunomodulatory cocktails on the generation of leukemia-derived dendritic cells, DC mediated T-cell activation and on-target/off-tumor toxicity.

Acute myeloid leukemia (AML) remains a devastating diagnosis in clear need of therapeutic advances. Both targeted dendritic cells (DC) and particularl...

Machine learning-based lactate-related genes signature predicts clinical outcomes and unveils novel therapeutic targets in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC), a predominant subtype of esophageal cancer, typically presents with poor prognosis. Lactate is a crucial me...

Artificial intelligence for diagnosis and predictive biomarkers in Non-Small cell lung cancer Patients: New promises but also new hurdles for the pathologist.

The rapid development of artificial intelligence (AI) based tools in pathology laboratories has brought forward unlimited opportunities for pathologis...

Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning.

BACKGROUND: Immune checkpoint inhibitors have proven efficacy against hepatitis B-virus positive hepatocellular. However, Immunotherapy-related advers...

InterDIA: Interpretable prediction of drug-induced autoimmunity through ensemble machine learning approaches.

Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to it...

Machine learning models for preventative mobile health asthma control.

INTRODUCTION: Asthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. Th...