Allergy & Immunology

Allergy

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

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Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, w...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell...

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious d...

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), h...

The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.

Immune checkpoint inhibitors (ICIs) are revolutionizing cancer treatment, and Artificial Intelligenc...

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and...

Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy.

Gliomas represent a highly aggressive class of tumors located in the brain. Despite the availability...

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and li...

Machine Learning-enhanced Signature of Metastasis-related T Cell Marker Genes for Predicting Overall Survival in Malignant Melanoma.

In this study, we aimed to investigate disparities in the tumor immune microenvironment (TME) betwee...

Automated real-world data integration improves cancer outcome prediction.

The digitization of health records and growing availability of tumour DNA sequencing provide an oppo...

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological maligna...

A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases.

Skin diseases are a significant global public health concern, affecting 21-85% of the world's popula...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the dise...

Alg-MFDL: A multi-feature deep learning framework for allergenic proteins prediction.

The escalating global incidence of allergy patients illustrates the growing impact of allergic issue...

Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors.

To date, the public health system has been impacted by the increasing costs of many diagnostic and t...

Machine learning-based new classification for immune infiltration of gliomas.

BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited ef...

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