biolearn
Advanced tools
Biolearn enables easy and versatile analyses of biomarkers of aging data. It provides tools to easily load data from publicly available sources like the
Gene Expression Omnibus <https://www.ncbi.nlm.nih.gov/geo/>, National Health and Nutrition Examimation Survey <https://www.cdc.gov/nchs/nhanes/index.htm>,
and the Framingham Heart Study <https://www.framinghamheartstudy.org/>. Biolearn also contains reference implemenations for common aging clock such at the
Horvath clock, DunedinPACE and many others that can easily be run in only a few lines of code. You can read more about it in our paper <https://www.biorxiv.org/content/10.1101/2023.12.02.569722v2>.
.. warning::
This is a prerelease version of the biolearn library. There may be bugs and interfaces are subject to change.
Python 3.10+
Install biolearn using pip.
.. code-block:: bash
pip install biolearn
To verify the library was installed correctly open python or a jupyter notebook and run:
.. code-block:: python
from biolearn.data_library import DataLibrary
If it executes with no errors then the library is installed. To get started check out some code examples <https://bio-learn.github.io/auto_examples/index.html>_
The biolearn team has a discord server <https://discord.gg/wZH85WRTxN>_ to answer questions,
discuss feature requests, or have any biolearn related discussions.
If you find any bugs with biolearn please create a Github issue including how we can replicate the issue and the expected vs actual behavior.
Detailed instructions on developer setup and how to contribute are available in the repo <https://github.com/bio-learn/biolearn/blob/master/DEVELOPMENT.md>_
FAQs
Machine learning for biomarkers computing
We found that biolearn demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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