Allergy

Biomimetic piezoelectric nanomaterial-modified oral microrobots for targeted catalytic and immunotherapy of colorectal cancer.

Lactic acid (LA) accumulation in the tumor microenvironment poses notable challenges to effective tu...

Integrative analysis of single-cell and bulk RNA sequencing unveils a machine learning-based pan-cancer major histocompatibility complex-related signature for predicting immunotherapy efficacy.

Major histocompatibility complex (MHC) could serve as a potential biomarker for tumor immunotherapy,...

Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning.

BACKGROUND: There is a significant imbalance of mitochondrial activity and oxidative stress (OS) sta...

Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.

The automation of pollen identification has seen vast improvements in the past years, with Convoluti...

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Desp...

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...

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.

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...

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling.

Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, po...

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...

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 ...

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...

Inference of Developmental Hierarchy and Functional States of Exhausted T Cells from Epigenetic Profiles with Deep Learning.

Exhausted T cells are a key component of immune cells that play a crucial role in the immune respons...