--- a/README.md
+++ b/README.md
@@ -1,52 +1,52 @@
-# 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}
-}
-```
+# 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](https://easymed.ai/models/AlyssaS/GOAT_multi-omics_biomarks/git/ci/main/tree/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}
+}
+```