LANDMark: An ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data
Project description
LANDMark
Implementation of a decision tree ensemble which splits each node using learned linear and non-linear functions.
Install
From PyPI:
pip install LANDMarkClassifier
From source:
git clone https://github.com/jrudar/LANDMark.git
cd LANDMark
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install .
Interface
An overview of the API can be found here.
Usage and Examples
Examples of how to use LANDMark
can be found here.
Contributing
To contribute to the development of LANDMark
please read our contributing guide
References
Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble
approach to the supervised selection of biomarkers in high-throughput sequencing data.
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn:
Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30.
Kuncheva LI, Rodriguez JJ. Classifier ensembles with a random linear oracle.
IEEE Transactions on Knowledge and Data Engineering. 2007;19(4):500–8.
Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42.
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