Gap-App

Gap-App predicts pancreatic cancer survival based on RNA-Seq gene expression data and patient sex using sex-specific machine learning models.

Key Features:

• Sex-specific predictive models: Separate 3-year survival predictive models for male and female pancreatic ductal adenocarcinoma patients achieve accuracies of 88.47% for males and 88.94% for females.
• Refined sex-specific models: Models refined using the most important features extracted from initial models achieve improved accuracies of 92.62% for males and 91.96% for females.
• Comprehensive model comparison: A single comprehensive model is provided alongside sex-specific models, with the refined comprehensive model achieving 87.84% accuracy.

Scientific Applications:

• Personalized cancer prognosis: Enables personalized survival predictions for pancreatic ductal adenocarcinoma patients based on individual RNA-Seq gene expression profiles combined with sex information.
• Sex-difference analysis in cancer: Supports investigation of distinct gene expression profiles between male and female pancreatic ductal adenocarcinoma patients for precision medicine approaches.

Methodology:

RNA-Seq gene expression data and advanced machine learning techniques were used to identify distinct gene expression profiles between male and female patients, followed by feature extraction from initial models to refine sex-specific predictive models.