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Biomarkers, Tumor

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Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma.

Genes and immunity
Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to av...

Explainable machine learning models for early gastric cancer diagnosis.

Scientific reports
Gastric cancer remains a significant global health concern, with a notably high incidence in East Asia. This paper explores the potential of explainable machine learning models in enhancing the early diagnosis of gastric cancer. Through comprehensive...

Deciphering dysregulation and CpG methylation in hepatocellular carcinoma using multi-omics and machine learning.

Epigenomics
This study investigates the altered expression and CpG methylation patterns of histone demethylase in hepatocellular carcinoma (HCC), aiming to uncover insights and promising diagnostics biomarkers. Leveraging TCGA-LIHC multi-omics data, we employe...

A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.

Scientific reports
Accurate prediction and grading of gliomas play a crucial role in evaluating brain tumor progression, assessing overall prognosis, and treatment planning. In addition to neuroimaging techniques, identifying molecular biomarkers that can guide the dia...

Utilising deep learning networks to classify ZEB2 expression images in cervical cancer.

British journal of hospital medicine (London, England : 2005)
Cervical cancer continues to be a significant cause of cancer-related deaths among women, especially in low-resource settings where screening and follow-up care are lacking. The transcription factor zinc finger E-box-binding homeobox 2 (ZEB2) has be...

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy.

Biomolecules
Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC p...

Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma.

PeerJ
OBJECTIVE: Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occur...

Biomarker profiling and integrating heterogeneous models for enhanced multi-grade breast cancer prognostication.

Computer methods and programs in biomedicine
BACKGROUND: Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effecti...

Self-organizing maps for exploration and classification of nuclear magnetic resonance spectra for untargeted metabolomics of breast cancer.

Journal of pharmaceutical and biomedical analysis
Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within met...

Machine learning and experimental analyses identified miRNA expression models associated with metastatic osteosarcoma.

Biochimica et biophysica acta. Molecular basis of disease
Osteosarcoma (OS), as the most common primary bone cancer, has a high invasiveness and metastatic potential, therefore, it has a poor prognosis. This study identified early diagnostic biomarkers using miRNA expression profiles associated with osteosa...