AIMC Topic: Biomarkers, Tumor

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Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.

Scientific reports
Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-grade glioma (LGG) is a less severe. The prognosis for glioma remains poor even after various treatment...

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration.

Pathology, research and practice
PURPOSE: Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and...

Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.

Immunologic research
Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given...

Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.

Frontiers in immunology
INTRODUCTION: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for imp...

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

Frontiers in immunology
BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene sign...

Identifying Potential Diagnostic and Therapeutic Targets for Infantile Hemangioma Using WGCNA and Machine Learning Algorithms.

Biochemical genetics
Infantile hemangioma (IH) is the most common benign vascular tumor during infancy and childhood and is characterized by abnormal vascular development. It is the most common vascular tumor and its related mechanisms and treatments remain a problem. IH...

Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabiliti...

CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques.

Computers in biology and medicine
Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the comp...