AIMC Topic: Biomarkers, Tumor

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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...

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles.

Analytical and bioanalytical chemistry
This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15-3 (CA 15-3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learni...

Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics.

Scientific reports
This study explores migrasomes' role in neuroblastoma, a common malignant tumor in children, and their potential impact on tumor formation. We analyzed neuroblastoma RNA-seq datasets from public databases, including GSE62564, GSE181559, target, and f...