Understanding Challenges in Preserving and Reconstructing Computer-Assisted Medical Decision Processes
| Autor: | Greg Hurst, William R. Cannon, Amanda M. White, Kelly Domico, Denise D. Schmoyer, W.H. McDonald, Brian S. Hooker, Kenneth J. Auberry, Mudita Singhal, Don S. Daly, Dale A. Pelletier, Deanna L. Auberry, Ronald C. Taylor, Jason E. McDermott |
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| Rok vydání: | 2007 |
| Předmět: |
Computer science
business.industry Association (object-oriented programming) Inference Biological network inference computer.software_genre Machine learning Pipeline (software) Protein protein interaction network ComputingMethodologies_PATTERNRECOGNITION Software Interaction network Software deployment Data mining Artificial intelligence business computer |
| Zdroj: | ICMLA |
| DOI: | 10.1109/icmla.2007.92 |
| Popis: | The Software Environment for Biological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and testing of network inference algorithms that use high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein- protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. In this paper, we present a case study on the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein- protein interaction networks from sets of mass spectrometry bait-prey experiment data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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