Allergy

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence rate and inadequate immunotherapy options. We analyzed mutation data of ESCC from public databases and employed 10 machine learning algorithms to generate 101 algorithm combinations. Based on the optimal range determined by the concordance index, we ra...

Prediction of PD-L1 expression in NSCLC patients using PET/CT radiomics and prognostic modelling for immunotherapy in PD-L1-positive NSCLC patients.

AIM: To develop a positron emission tomography/computed tomography (PET/CT)-based radiomics model for predicting programmed cell death ligand 1 (PD-L1) expression in non-small cell lung cancer (NSCLC) patients and estimating progression-free survival (PFS) and overall survival (OS) in PD-L1-positive patients undergoing first-line immunotherapy.

Artificial intelligence predicts multiclass molecular signatures and subtypes directly from breast cancer histology: a multicenter retrospective study.

Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict clas...

A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.

The growing application of immune checkpoint inhibitors (ICIs) in cancer immunotherapy has underscored the critical need for reliable methods to ident...

Deciphering aging-associated prognosis and heterogeneity in gastric cancer through a machine learning-driven approach.

Gastric cancer (GC) is a prevalent malignancy with a high mortality rate and limited treatment options. Aging significantly contributes to tumor progr...

Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer.

Intratumor heterogeneity (ITH) is involved in tumor evolution and drug resistance. Drug sensitivity shows discrepancy in different breast cancer (BRCA...

Bibliometric and LDA analysis of acute rejection in liver transplantation: Emerging trends, immunotherapy challenges, and the role of artificial intelligence.

With the rising demand for liver transplantation (LT), research on acute rejection (AR) has become increasingly diverse, yet no consensus has been rea...

The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer.

Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limit...

Pan-cancer analysis of CDC7 in human tumors: Integrative multi-omics insights and discovery of novel marine-based inhibitors through machine learning and computational approaches.

Cancer remains a significant global health challenge, with the Cell Division Cycle 7 (CDC7) protein emerging as a potential therapeutic target due to ...

Machine Learning and Mendelian Randomization Reveal a Tumor Immune Cell Profile for Predicting Bladder Cancer Risk and Immunotherapy Outcomes.

This study's objective was to develop predictive models for bladder cancer (BLCA) using tumor infiltrated immune cell (TIIC)-related genes. Multiple R...

Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis.

The increasing global incidence of atopic dermatitis (AD) in children, especially in Western industrialized nations, has attracted considerable attent...

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for indi...

Revealing gut microbiota biomarkers associated with melanoma immunotherapy response and key bacteria-fungi interaction relationships: evidence from metagenomics, machine learning, and SHAP methodology.

INTRODUCTION: The gut microbiota is associated with the response to immunotherapy in cutaneous melanoma (CM). However, gut fungal biomarkers and bacte...

Aggregation induced emission luminogen bacteria hybrid bionic robot for multimodal phototheranostics and immunotherapy.

Multimodal phototheranostics utilizing single molecules offer a "one-and-done" approach, presenting a convenient and effective strategy for cancer the...

Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.

BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial...

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening pati...

Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems.

Model-agnostic meta-learning (MAML), coupled with digital twins, is transformative for predictive maintenance (PdM), especially in robotic arms in ass...

Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.

Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditiona...

Multimodal deep learning for predicting PD-L1 biomarker and clinical immunotherapy outcomes of esophageal cancer.

Although the immune checkpoint inhibitors (ICIs) have demonstrated remarkable anti-tumor efficacy in solid tumors, the proportion of ESCC patients who...

Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major...