bioRxivBioinformaticsPreprint
G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data
Shuai ZengiD, Trinath Adusumilli, Sania Zafar Awan, Manish Sridhar ImmadiiD, Dong He XuiD, Trupti JoshiiD
Abstract
The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. 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 and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.
- organism: human
- organism: plant (other)
- organism: virus
- assay: multiome
- research focus: drug response / drug discovery
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: deep learning
- Citations
- 3
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- DeepGS: Predicting phenotypes from genotypes using Deep Learning
Wenlong Ma, Zhixu Qiu, … Chuang Ma·PlantaPeer-reviewed·Aug 12, 2018
- OmiXAI: An Ensemble XAI Pipeline for Interpretable Deep Learning in Omics Data
Ameliia Alaeva, Natalya Mikhaylovskaya, … Maria S. Poptsova·Scientific ReportsPeer-reviewed·Jul 15, 2026
- MultiOmicsAgent: Guided extreme gradient-boosted decision trees-based approaches for biomarker-candidate discovery in multi-omics data
Jens Settelmeier, Sandra Goetze, … Bernd Wollscheid·bioRxiv·Jul 24, 2024
- Janggu: Deep Learning for Genomics
Wolfgang Kopp, Remo Monti, … Altuna Akalin·Nature CommunicationsPeer-reviewed·Jul 13, 2020
- DeepGAMI: Deep biologically guided auxiliary learning for multimodal integration and imputation to improve phenotype prediction
Pramod Bharadwaj Chandrashekar, Jiebiao Wang, … Daifeng Wang·Genome MedicinePeer-reviewed·Oct 30, 2023
- BiomarkerML: A cloud-based proteomics ML workflow for biomarker discovery
Yuhan Zhou, Anand Kumar Maurya, … Avigail Taylor·bioRxiv·Oct 16, 2025