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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 <a href="https://github.com/DabinJeong/GOAT2.0"> here </a>, please.
+
+![workflow](./img/method_overview.png)  
+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**](https://academic.oup.com/bioinformatics/article/39/10/btad582/7280697)
+## 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, <a href="https://github.com/DabinJeong/Comparative_analysis_multi-omics_biomarker"> comparative_analysis_multi-omics_biomarker</a>.
+
+
+## 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}
+}
+```