10.1093/bioinformatics/btaa942
https://zenodo.org/records/4748570
oai:zenodo.org:4748570
Manica, M.
M.
Manica
IBM Research Europe
Bunne, C.
C.
Bunne
IBM Research Europe
Mathis, R.
R.
Mathis
IBM Research Europe
Cadow, J.
J.
Cadow
IBM Research Europe
Ahsen, M.
M.
Ahsen
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Stolovitzky
Stolovitzky
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai
Martínez, M.
M.
Martínez
IBM Research Europe
COSIFER: a Python package for the consensus inference of molecular interaction networks
Zenodo
2020
2020-11-02
eng
https://zenodo.org/communities/ipc
https://zenodo.org/communities/eu
Creative Commons Attribution 4.0 International
The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks.
European Commission
10.13039/501100000780
826121
individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology