License
This work is under a BSD-3-Clause license.
Main Libraries used
- Django
- scikit-learn : Scikit-learn: Machine Learning in Python, Pedregosa et al., JMLR 12, pp. 2825-2830, 2011.
- Celery (and django-celery)
- Alipy : ALiPy: Active learning in python. Tang, Y.-P.; Li, G.-X.; and Huang, S.-J. Technical report, Nanjing University of Aeronautics and Astronautics, 2019. Available as arXiv preprint https://arxiv.org/abs/1901.03802.
Funding
This project was realised at the Interuniversity Institute of Bioinformatics in Brussels (IB2), a collaborative bioinformatics research initiative between Université Libre de Bruxelles (ULB) and Vrije Universiteit Brussel (VUB). Basic architecture and design was largely inspired by the work done by Alexandre Renaux for ORVAL. This work was supported by Service Public de Wallonie Recherche under grant n° 2010235 - ARIAC by DIGITALWALLONIA4.AI.
 |  |
 |
Cite us
If you used our tool (and liked it), please cite us with :
Nachtegael, C., De Stefani, J., & Lenaerts, T. (2022). ALAMBIC (Version 0.1.0-alpha) [Computer software]. https://doi.org/10.5281/zenodo.7394114or in the format Bibtex :
@software{Nachtegael_ALAMBIC_2022,
author = {Nachtegael, Charlotte and De Stefani, Jacopo and Lenaerts, Tom},
doi = {10.5281/zenodo.7394114},
month = {12},
title = ,
url = {https://github.com/Trusted-AI-Labs/ALAMBIC},
version = {0.1.0-alpha},
year = {2022}
}