COSIFER: a Python package for the consensus inference of molecular interaction networks.
- Manica M Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
- Bunne C Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
- Mathis R Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
- Cadow J Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
- Ahsen ME Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA.
- Stolovitzky GA Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029-5674, USA.
- Martínez MR Cognitive Computing and Industry Solutions, IBM Research Europe, Rüschlikon, ZH 8803, Switzerland.
- 2020-11-26
Published in:
- Bioinformatics (Oxford, England). - 2020
Statistics and Probability
Computational Theory and Mathematics
Biochemistry
Molecular Biology
Computational Mathematics
Computer Science Applications
English
SUMMARY
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.
AVAILABILITY AND IMPLEMENTATION
COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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.
AVAILABILITY AND IMPLEMENTATION
COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
- Language
-
- English
- Open access status
- hybrid
- Identifiers
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- DOI 10.1093/bioinformatics/btaa942
- PMID 33241320
- Persistent URL
- https://sonar.ch/global/documents/66674
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