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GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype

Note

GOAT 2.0 has been released. Checkout here , please.

workflow
We propose a novel deep graph attention model for biomarker discovery for the asthma subtype by incorporating complex interactions between biomolecules and capturing key biomarker candidates using the attention mechanism.

Full manuscript available here

Setup

Create docker image

You can build a docker image from Dockerfile.

# Pull base image from docker hub
docker pull dabinjeong/cuda:10.1-cudnn7-devel-ubuntu18.04

# Build docker image
docker build --tag biomarker:0.1.1 .

You can also download the docker image from Docker hub (https://hub.docker.com/repository/docker/dabinjeong/biomarker/general).

docker pull dabinjeong/biomarker:0.1.1

Install workflow manager: Nextflow

conda create -n biomarker python=3.9
conda activate biomarker
conda install -c bioconda nextflow=21.04.0

Run

nextflow run biomarker_discovery.nf -c pipeline.config -with-docker biomarker:0.1.1

Comparitive analysis

For comparative analysis, please refer to the following repository, comparative_analysis_multi-omics_biomarker.

Citation

@article{jeong2023goat,
  title={GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype},
  author={Jeong, Dabin and Koo, Bonil and Oh, Minsik and Kim, Tae-Bum and Kim, Sun},
  journal={Bioinformatics},
  volume={39},
  number={10},
  pages={btad582},
  year={2023},
  publisher={Oxford University Press}
}