MINDS is a framework designed to integrate multimodal oncology data. It queries and integrates data from multiple sources, including clinical data, genomic data, and imaging data from the NIH NCI CRDC and TCIA portals.

Installation

Currently the cloud version of MINDS is in closed beta, but, you can still recreate the MINDS database locally. To get the local version of the MINDS database running, you will need to setup a MySQL database and populate it with the MINDS schema. This can be easily done using a docker container. First, you will need to install docker. You can find the installation instructions for your operating system here. Next, you will need to pull the MySQL docker image and run a container with the following command.

NOTE: Please replace my-secret-pw with your desired password and port with the port you want to use to access the database. The default port for MySQL is 3306. The following command will not work until you replace port with a valid port number.

docker run -d --name minds -e MYSQL_ROOT_PASSWORD=my-secret-pw -e MYSQL_DATABASE=minds -p port:3306 mysql

Finally, to install the MINDS python package use the following pip command:

pip install git+https://github.com/lab-rasool/MINDS.git

After installing the package, please create a .env file in the root directory of the project with the following variables:

HOST=127.0.0.1
PORT=3306
DB_USER=root
PASSWORD=my-secret-pw
DATABASE=minds   

Usage

Initial setup and automated updates

If you have locally setup the MINDS database, then you will need to populate it with data. To do this, or to update the database with the latest data, you can use the following command:

# Import the minds package
import minds

# Update the database with the latest data
minds.update()

Querying the MINDS database

The MINDS python package provides a python interface to the MINDS database. You can use this interface to query the database and return the results as a pandas dataframe.

import minds

# get a list of all the tables in the database
tables = minds.get_tables()

# get a list of all the columns in a table
columns = minds.get_columns("clinical")

# Query the database directly
query = "SELECT * FROM nihnci.clinical WHERE project_id = 'TCGA-LUAD' LIMIT 10"
df = minds.query(query)

Building the cohort and downloading the data

# Generate a cohort to download from query
query_cohort = minds.build_cohort(query=query, output_dir="./data")

# or you can now directly supply a cohort from GDC
gdc_cohort = minds.build_cohort(gdc_cohort="cohort_Unsaved_Cohort.2024-02-12.tsv", output_dir="./data")

# to get the cohort details
cohort.stats()

# to download the data from the cohort to the output directory specified
# you can also specify the number of threads to use and the modalities to exclude or include
cohort.download(threads=12, exclude=["Slide Image"])

Please cite our work

@Article{s24051634,
    AUTHOR = {Tripathi, Aakash and Waqas, Asim and Venkatesan, Kavya and Yilmaz, Yasin and Rasool, Ghulam},
    TITLE = {Building Flexible, Scalable, and Machine Learning-Ready Multimodal Oncology Datasets},
    JOURNAL = {Sensors},
    VOLUME = {24},
    YEAR = {2024},
    NUMBER = {5},
    ARTICLE-NUMBER = {1634},
    URL = {https://www.mdpi.com/1424-8220/24/5/1634},
    ISSN = {1424-8220},
    DOI = {10.3390/s24051634}
}

Contributing

We welcome contributions from the community. If you would like to contribute to the MINDS project, please read our contributing guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.