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GOAT_multi-omics_biomarks

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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](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}
}
```

	a/README.md		b/README.md
1	# GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype	1	# GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype
2	> [!NOTE]	2	[!NOTE]
3	> GOAT 2.0 has been released. Checkout <a href="https://github.com/DabinJeong/GOAT2.0"> here </a>, please.	3	GOAT 2.0 has been released. Checkout <a href="https://github.com/DabinJeong/GOAT2.0"> here </a>, please.
4		4
5	![workflow](./img/method_overview.png)	5	![workflow](https://easymed.ai/models/AlyssaS/GOAT_multi-omics_biomarks/git/ci/main/tree/img/method_overview.png)
6	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.	6	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.
7		7
8	Full manuscript available [here](https://academic.oup.com/bioinformatics/article/39/10/btad582/7280697)	8	Full manuscript available [here](https://academic.oup.com/bioinformatics/article/39/10/btad582/7280697)
9	## Setup	9	## Setup
10	### Create docker image	10	### Create docker image
11	You can build a docker image from Dockerfile.	11	You can build a docker image from Dockerfile.
12	~~~	12	~~~
13	# Pull base image from docker hub	13	# Pull base image from docker hub
14	docker pull dabinjeong/cuda:10.1-cudnn7-devel-ubuntu18.04	14	docker pull dabinjeong/cuda:10.1-cudnn7-devel-ubuntu18.04
15		15
16	# Build docker image	16	# Build docker image
17	docker build --tag biomarker:0.1.1 .	17	docker build --tag biomarker:0.1.1 .
18	~~~	18	~~~
19	You can also download the docker image from Docker hub (https://hub.docker.com/repository/docker/dabinjeong/biomarker/general).	19	You can also download the docker image from Docker hub (https://hub.docker.com/repository/docker/dabinjeong/biomarker/general).
20	~~~	20	~~~
21	docker pull dabinjeong/biomarker:0.1.1	21	docker pull dabinjeong/biomarker:0.1.1
22	~~~	22	~~~
23	### Install workflow manager: Nextflow	23	### Install workflow manager: Nextflow
24	~~~	24	~~~
25	conda create -n biomarker python=3.9	25	conda create -n biomarker python=3.9
26	conda activate biomarker	26	conda activate biomarker
27	conda install -c bioconda nextflow=21.04.0	27	conda install -c bioconda nextflow=21.04.0
28	~~~	28	~~~
29		29
30	## Run	30	## Run
31	~~~	31	~~~
32	nextflow run biomarker_discovery.nf -c pipeline.config -with-docker biomarker:0.1.1	32	nextflow run biomarker_discovery.nf -c pipeline.config -with-docker biomarker:0.1.1
33	~~~	33	~~~
34		34
35		35
36	## Comparitive analysis	36	## Comparitive analysis
37	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>.	37	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>.
38		38
39		39
40	## Citation	40	## Citation
41	```	41	```
42	@article{jeong2023goat,	42	@article{jeong2023goat,
43	title={GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype},	43	title={GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype},
44	author={Jeong, Dabin and Koo, Bonil and Oh, Minsik and Kim, Tae-Bum and Kim, Sun},	44	author={Jeong, Dabin and Koo, Bonil and Oh, Minsik and Kim, Tae-Bum and Kim, Sun},
45	journal={Bioinformatics},	45	journal={Bioinformatics},
46	volume={39},	46	volume={39},
47	number={10},	47	number={10},
48	pages={btad582},	48	pages={btad582},
49	year={2023},	49	year={2023},
50	publisher={Oxford University Press}	50	publisher={Oxford University Press}
51	}	51	}
52	```	52	```