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This article is part of the supplement: Computational Intelligence in Bioinformatics and Biostatistics: new trends from the CIBB conference series

Open Access Research

A knowledge-based decision support system in bioinformatics: an application to protein complex extraction

Antonino Fiannaca1, Massimo La Rosa1, Alfonso Urso1*, Riccardo Rizzo1 and Salvatore Gaglio12

Author Affiliations

1 ICAR-CNR, National Research Council of Italy, Viale delle Scienze Ed. 11, Palermo, 90128, Italy

2 DICGIM, University of Palermo, Viale delle Scienze Ed. 6, Palermo, 90128, Italy

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BMC Bioinformatics 2013, 14(Suppl 1):S5  doi:10.1186/1471-2105-14-S1-S5

Published: 14 January 2013

Abstract

Background

We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems.

Results

We briefly present the KDSS' architecture and basic concepts used in the design of the knowledge base and the reasoning component. The system is then tested using a subset of Saccharomyces cerevisiae Protein-Protein interaction dataset. We used this subset because it has been well studied in literature by several research groups in the field of complex extraction: in this way we could easily compare the results obtained through our KDSS with theirs. Our system suggests both a preprocessing and a clustering strategy, and for each of them it proposes and eventually runs suited algorithms. Our system's final results are then composed of a workflow of tasks, that can be reused for other experiments, and the specific numerical results for that particular trial.

Conclusions

The proposed approach, using the KDSS' knowledge base, provides a novel workflow that gives the best results with regard to the other workflows produced by the system. This workflow and its numeric results have been compared with other approaches about PPI network analysis found in literature, offering similar results.