Naive Bayes classifier for Laplacian-modified models
Efficient, scikit-learn compatible, and designed for binary/boolean data
LaplacianNB is a Python module developed at Novartis AG for a Naive Bayes classifier for Laplacian-modified models, based on the scikit-learn Naive Bayes implementation.
This classifier is ideal for binary/boolean data, using only the indices of positive bits for efficient prediction. The algorithm was first implemented in Pipeline Pilot and KNIME.
- Naive Bayes classifier for Laplacian-modified models
- Optimized for binary/boolean data
- Fast prediction using indices of positive bits
- scikit-learn compatible API
- Lightweight and easy to integrate
Install the latest release from PyPI:
pip install laplaciannbNidhi; Glick, M.; Davies, J. W.; Jenkins, J. L. Prediction of biological targets
for compounds using multiple-category Bayesian models trained on chemogenomics
databases. J. Chem. Inf. Model. 2006, 46, 1124– 1133,
https://doi.org/10.1021/ci060003g
Lam PY, Kutchukian P, Anand R, et al. Cyp1 inhibition prevents doxorubicin-induced cardiomyopathy
in a zebrafish heart-failure model. Chem Bio Chem. 2020:cbic.201900741.
https://doi.org/10.1002/cbic.201900741
- Bartosz Baranowski (bartosz.baranowski@novartis.com)
- Edgar Harutyunyan (edgar.harutyunyan_ext@novartis.com)
v0.6.1- Fixes for scikit-learn 1.7, rdkit 2025+ compatibility, move to uv buildv0.6.0- Move to pdm buildv0.5.0- Initial release
This project is licensed under the BSD 3-Clause License. See the LICENSE file for details.