About BiCoN
Table of contents
BiCoN algorithm
Unsupervised learning approaches are frequently employed to identify patient subgroups and
biomarkers such as disease-associated genes. Biclustering is a powerful technique often used
with
expression data to cluster genes along with patients. However, the genes forming biclusters are
often not functionally related, complicating interpretation of the results.
To alleviate this, we developed the network-constrained biclustering approach BiCoN which
(i)
restricts biclusters to functionally related genes connected in molecular interaction networks
and
(ii) maximizes the expression difference between two subgroups of patients.
Web application
BiCoN-web allows you to use the full analysis power of BiCoN combined with an intuitive interface. To learn how to use BiCoN-web please visit our Documentation.The source code fo this exact web application can be found on GitHub under biomedbigdata/BiCoN-web.
This instance of BiCoN-web is running on version 1.2.1 which
itself uses
BiCoN package version 1.3.4 and the following key packages:
- Web tools: Django, Celery, Gunicorn, psycopg2, Docker, PostgreSQL, Nginx
- Data access packages: ndex2, pybiomart, mygene
- Analysis tools: pandas, NumPy, NetworkX, lifelines
- Visualisation tools: Matplotlib, seaborn, Plotly
PyPI package
If you prefer to run BiCoN locally and in python, then just install BiCoN from PyPI:pip install bicon
Please check the instruction for the package at our GitHub repository.
Cite
BiCoN was developed by the Big Data in BioMedicine group and the Computational Systems Medicine group at the Chair of Experimental Bioinformatics.If you use BiCoN in your research, we kindly ask you to cite the following manuscript:
Olga Lazareva, Stefan Canzar, Kevin Yuan, Jan Baumbach, David B Blumenthal, Paolo Tieri, Tim
Kacprowski*, Markus List*,
BiCoN: Network-constrained biclustering of patients and omics data, Bioinformatics, 2020;,
btaa1076, https://doi.org/10.1093/bioinformatics/btaa1076
** joint last author