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Related Experiment Video

Updated: May 16, 2025

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

Published on: January 12, 2017

An efficient graph attention framework enhances bladder cancer prediction.

Taghreed S Ibrahim¹, M S Saraya², Ahmed I Saleh²

¹Computers and Control Dept. faculty of engineering, Mansoura University, Mansoura, Egypt. taghreedaboelnaga79@gmail.com.

Scientific Reports

|April 1, 2025

Summary

This summary is machine-generated.

This study introduces MSL-GAT, a novel graph neural network, for personalized bladder cancer prediction by identifying driver genes. It achieves high accuracy, aiding early detection and targeted therapies.

Keywords:

Attention mechanism Bladder cancer Cancer prediction Driver genes Graph convolutional neural network (GCNN)

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Last Updated: May 16, 2025

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

A Murine Orthotopic Bladder Tumor Model and Tumor Detection System

Published on: January 12, 2017

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

Evaluation of the Efficacy of the H. pylori Protein HP-NAP as a Therapeutic Tool for Treatment of Bladder Cancer in an Orthotopic Murine Model

Evaluation of the Efficacy of the H. pylori Protein HP-NAP as a Therapeutic Tool for Treatment of Bladder Cancer in an Orthotopic Murine Model

Published on: May 29, 2015

Area of Science:

Oncology
Bioinformatics
Genetics

Background:

Bladder cancer is a prevalent malignancy with complex genetic underpinnings.
Accurate prediction and treatment are critical due to rapid metastasis.
Identifying personalized driver genes (PDGs) is key for effective intervention.

Purpose of the Study:

To develop a novel method for identifying personalized driver genes (PDGs) in bladder cancer.
To enhance the prediction of bladder cancer at the individual patient level.
To leverage multi-omics data for a comprehensive understanding of bladder cancer's molecular landscape.

Main Methods:

Utilized a novel graph neural network (GNN) with attention mechanisms, termed Multi Stacked-Layered GAT (MSL-GAT).
Employed attention mechanisms to extract features from coding and non-coding genes, including long non-coding RNAs (lncRNAs).
Integrated genomic, transcriptomic, and epigenomic data for PDG detection and binary classification.

Main Results:

MSL-GAT achieved 97.72% accuracy, outperforming classical and deep learning methods on the TCGA-BLCA benchmark.
The model demonstrated improved specificity and sensitivity in bladder cancer prediction.
Precisely classified PDGs crucial for cancer cell survival and proliferation.

Conclusions:

MSL-GAT effectively identifies critical driver genes for personalized bladder cancer prediction.
The approach facilitates the discovery of novel therapeutic targets, such as lncRNAs for RNA interference (RNAi).
This method can assist physicians in early bladder cancer detection and inform targeted treatment strategies.