Unsupervised learning approaches are frequently employed to identify patient subgroups and
biomarkers such as disease-associated genes. Biclustering is a powerful technique often used
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.
This instance of BiCoN-web is running on version
BiCoN package version
1.3.4 and the following key packages:
pip install bicon
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