GeneFromAPI

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GeneFromAPI is website for gene searching with open API. It used DrugProtAI, DrugTar, Harmonizome and OpenTarget API. Especially only CTD Gene-Disease Associations Dataset was used from Harmonizome.

Using the GeneFromAPI Docker image

Now, only docker is available to use GeneFromAPI. If docker is not installed, please install docker at first. Installation commands for docker are written in docker_install.sh. When this script is not worked so that docker is not installed, please check the comment of that script. If the recommended command is not work, please install the docker by another way.

Please clone the git repository to your workspace.

git clone https://github.com/khsjh/GeneFromAPI.git

And build docker with gene_explorer directory

docker compose -f docker_compose.yml up -d --build # Build the docker imange and generate container

You can check whether docker is built successfully. If "UP" is displayed in the STATUS column, the work is done.

docker compose -f docker_compose.yml ps # Check docker status

# output
AME                IMAGE               COMMAND                  SERVICE   CREATED          STATUS          PORTS
gene_explorer_api   gene_explorer-api   "uvicorn main:app --…"   api       55 minutes ago   Up 55 minutes   8000/tcp
gene_explorer_web   nginx:alpine        "/docker-entrypoint.…"   web       55 minutes ago   Up 49 minutes   0.0.0.0:8080->80/tcp, [::]:8080->80/tcp

Now, you can access the GeneFromAPI with your local address

http://localhost:8080

You can terminate the docker by below command.

docker compose -f docker_compose.yml down # Delete docker container

Reference

Diamant I, Clarke DJB, Evangelista JE, Lingam N, Ma'ayan A. Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources. Nucleic Acids Res. 2024 Nov 20. pii: 53(1):D1016-D1028.

Buniello, A. et al. (2025). Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research.

Ankit Halder, Sabyasachi Samantaray, Sahil Barbade, Aditya Gupta, Sanjeeva Srivastava, DrugProtAI: A machine learning–driven approach for predicting protein druggability through feature engineering and robust partition-based ensemble methods, Briefings in Bioinformatics, Volume 26, Issue 4, July 2025, bbaf330, https://doi.org/10.1093/bib/bbaf330

Niloofar Borhani, Iman Izadi, Ali Motahharynia, Mahsa Sheikholeslami, Yousof Gheisari, DrugTar improves druggability prediction by integrating large language models and gene ontologies, Bioinformatics, Volume 41, Issue 7, July 2025, btaf360, https://doi.org/10.1093/bioinformatics/btaf360