Journal ArticleG2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics DataZeng, Shuai; Adusumilli, Trinath; Awan, Sania Zafar; Immadi, Manish Sridhar; Xu, Dong (ORCID:0000000248090514); Joshi, Trupti (ORCID:0000000189444924)<p>Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.</p>Biomolecules2025-12-0110658045Biomolecules151216732218-273Xhttps://doi.org/10.3390/biom151216732343815National Science Foundation