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biolearn

Machine learning for biomarkers computing

pipPyPI
Version
0.6.4
Maintainers
2

Biolearn

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.
  • Source code: https://github.com/bio-learn/biolearn/
  • Documentation Homepage: https://bio-learn.github.io/

Requirements

Python 3.10+

Install

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>_

Discord server

The biolearn team has a discord server <https://discord.gg/wZH85WRTxN>_ to answer questions, discuss feature requests, or have any biolearn related discussions.

Issues

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.

Contributing

Detailed instructions on developer setup and how to contribute are available in the repo <https://github.com/bio-learn/biolearn/blob/master/DEVELOPMENT.md>_

Keywords

biomarker

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