Published November 2, 2020 | Version v1
Journal article Open

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

  • 1. IBM Research Europe
  • 2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai


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.



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iPC – individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology 826121
European Commission