This study focuses on the potential of multi-omics and machine learning approaches in
improving our understanding of the malting processes and cultivation systems in barley. The …

Abstract Machine learning (ML) and nanotechnology interfacing are exploring opportunities
for cancer treatment strategies. To improve cancer therapy, this article investigates the …

Radiomic datasets can be class-imbalanced, for instance, when the prevalence of diseases
varies notably, meaning that the number of positive samples is much smaller than that of …

Simple Summary Lung cancer is one of the most common cancers and can be difficult to
treat. One of the treatment methods uses drugs that target a protein called the epidermal …

Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor
progression, assessing overall prognosis, and treatment planning. In addition to …

Objectives This study evaluates the accuracy of radiomics in predicting lymph node
metastasis in non-small cell lung cancer, which is crucial for patient management and …

This study focuses on the use of machine learning (ML) models to predict the biodistribution
of nanoparticles in various organs, using a dataset derived from research on nanoparticle …

Rationale and Objectives This study aimed to non-invasively predict epidermal growth factor
receptor (EGFR) mutation status in patients with lung adenocarcinoma using multi-phase …

Objective This study aimed to evaluate the value of 18 F-FDG PET/CT radiomics in
predicting EGFR gene mutations in non-small cell lung cancer by meta-analysis. Methods …

Background Although several studies have confirmed the prognostic value of the
consolidation to tumor ratio (CTR) in non-small cell lung cancer (NSCLC), there still remains …