An analysis pipeline for the inference of protein-protein interaction networks
| Autor: | Jason M. Gilmore, Brian S. Hooker, Kelly Domico, Mudita Singhal, Ronald C. Taylor, Kenneth J. Auberry, H. Steven Wiley, Deanna L. Auberry, William R. Cannon, Denise D. Schmoyer, Don S. Daly, Greg Hurst, W. Hayes McDonald, Jason E. McDermott, Dale A. Pelletier, Amanda M. White |
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| Rok vydání: | 2010 |
| Předmět: |
Computer science
Bayesian probability Inference Library and Information Sciences computer.software_genre Machine learning General Biochemistry Genetics and Molecular Biology Mass Spectrometry Software Protein Interaction Mapping Databases Protein business.industry Computational Biology Proteins Biological network inference Pipeline (software) ComputingMethodologies_PATTERNRECOGNITION Workflow Software deployment Data mining Artificial intelligence business computer Biological network Algorithms Information Systems |
| Zdroj: | ResearcherID |
| ISSN: | 1748-5673 |
| Popis: | We present a platform for the reconstruction of protein-protein interaction networks inferred from Mass Spectrometry (MS) bait-prey data. The Software Environment for Biological Network Inference (SEBINI), an environment for the deployment of network inference algorithms that use high-throughput data, forms the platform core. Among the many algorithms available in SEBINI is the Bayesian Estimator of Probabilities of Protein-Protein Associations (BEPro3) algorithm, which is used to infer interaction networks from such MS affinity isolation data. Also, the pipeline incorporates the Collective Analysis of Biological Interaction Networks (CABIN) software. We have thus created a structured workflow for protein-protein network inference and supplemental analysis. |
| Databáze: | OpenAIRE |
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