4.7 Article

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

期刊

BIOINFORMATICS
卷 37, 期 14, 页码 2070-2072

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OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btaa942

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  1. European Union [668858, 826121]

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The emergence of high-throughput technologies allows for measurements of numerous molecular entities, but inferring networks from this data remains a challenge. COSIFER is a package that offers a selection of network inference methodologies and consensus strategies to create robust network predictions.
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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