G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Integrating and analyzing multi-omics data for phenotype prediction and marker discovery is complex.
• Accessible tools are needed to combine deep learning, hyperparameter optimization, visualization, and downstream analysis.

Purpose of the Study:

• To develop G2PDeep-v2, a unified web platform for phenotype prediction and marker discovery using multi-omics data.
• To provide researchers with an accessible tool for analyzing complex traits across various organisms.

Main Methods:

• Developed G2PDeep-v2, a web-based platform utilizing deep learning.
• Implemented an interactive interface for creating and training deep learning models.
• Integrated automated hyperparameter tuning on high-performance computing resources.
• Included visualization tools for phenotype and marker predictions.
• Enabled Gene Set Enrichment Analysis for significant markers.

Main Results:

• G2PDeep-v2 facilitates phenotype prediction and marker discovery from multi-omics data.
• The platform supports analysis across diverse organisms, including humans and plants.
• Automated hyperparameter tuning and visualization enhance the analysis workflow.

Conclusions:

• G2PDeep-v2 offers a powerful and accessible solution for multi-omics data analysis.
• The platform aids in understanding the molecular mechanisms of complex traits and diseases.
• It empowers researchers to advance studies in genomics and computational biology.