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:

• Multi-omics data integration is crucial for understanding complex biological phenotypes.
• Predictive modeling and marker discovery are essential for biological research and disease understanding.

Purpose of the Study:

• To introduce G2PDeep-v2, a novel deep learning-based web server.
• To provide a user-friendly platform for phenotype prediction and marker discovery from multi-omics data in any organism.

Main Methods:

• Development of a web-based platform utilizing deep learning algorithms.
• Implementation of an interactive interface for model creation and training.
• Integration of automated hyperparameter tuning and high-performance computing resources.
• Inclusion of visualization tools and Gene Set Enrichment Analysis (GSEA).

Main Results:

• G2PDeep-v2 enables phenotype prediction and genetic marker discovery across diverse organisms.
• The platform facilitates the exploration of molecular mechanisms underlying biological phenotypes.
• Users can generate and train deep learning models with automated hyperparameter optimization.

Conclusions:

• G2PDeep-v2 offers a powerful and accessible tool for multi-omics data analysis.
• The server supports biological research by providing insights into genotype-phenotype relationships.
• It serves as a valuable resource for researchers studying complex traits and diseases in various species.