Published April 2, 2019
| Version v2
Dataset
Open
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
- 1. Bioinformatics and Modelling, Luxembourg Institute of Health, and Mathematical Modelling of Cellular Processes, Max Delbrück Center for Molecular Medicine in the Helmholtz Association
- 2. School of Computer Science and Engineering, Nanyang Technological University
- 3. Bioinformatics and Modelling, Luxembourg Institute of Health
Description
Baum_et_al_2019_Supplementary_Figures.pdf: Supplementary Figures S1 and S2. Legends are included under each figure.
sbm-for-correlation-based-networks-master.zip: Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.
Files
Baum_et_al_2019_Supplementary_Figures.pdf
Files
(1.3 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:ee8246bcb81ac40d7420e87e63755d7c
|
476.9 kB | Preview Download |
|
md5:d7a0626eba8d0991e37abeda23f452a3
|
787.2 kB | Preview Download |
Additional details
References
- Budczies J, Brockmoller SF, et al. (2013). Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: alterations in glutamine and beta-alanine metabolism. J Proteomics. 2013;94:279-88.
- Peixoto TP (2014). The graph-tool python library. 10.6084/m9.figshare.1164194