COSIFER: A Python package for the consensus inference of molecular interaction networks

Matteo Manica, Charlotte Bunne, Roland Mathis, Joris Cadow, Mehmet Eren Ahsen, Gustavo A. Stolovitzky, María Rodríguez Martínez

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)2070-2072
Number of pages3
Issue number14
StatePublished - Jul 15 2021
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


Dive into the research topics of 'COSIFER: A Python package for the consensus inference of molecular interaction networks'. Together they form a unique fingerprint.

Cite this