### PEPPER
PEPPER - Protein complex Expansion using Protein-Protein intERaction networks - **identifies meaningful pathways / complexes** as densely connected sub-networks from *seed* lists of proteins derived from pull-down assays (*i.e* AP-MS...).
PEPPER resolves connection sub-graph discovery problems by using **multi-objective optimisation** involving two objective functions: (i) the **coverage**, a solution must contain as many proteins from the *seed* as possible, (ii) the **density**, a solution must contain as many interactions as possible. Since these objectives conflict, no single solution can be considered as dominating the others. To summarise the information from all solutions, PEPPER merges *Pareto solutions* into a final predicted protein complex by **maximising the modularity** using a greedy search.
PEPPER further refines predictions by an integrated **structure- and function-based post-processing** pipeline to rank proteins that were added by the algorithm. This workflow orchestrates **external data integration** (GO annotations, known complexes matching) besides measuring **connectivity features** (topological coefficients).
**References:**
- C. Winterhalter, R. Nicolle, A. Louis, C. To, F. Radvanyi, and M. Elati. "PEPPER: Cytoscape app for Protein complex Expansion using Protein-Protein intERaction networks", *Bioinformatics*, August 2014. DOI: [http://bioinformatics.oxfordjournals.org/content/early/2014/09/01/bioinformatics.btu517 10.1093/bioinformatics/btu517]
- M. Elati, C. To and R. Nicolle, Multi-objective optimization for relevant sub-graph extraction.
In Learning and Intelligent OptimizatioN (LION'7), LNCS, Italy, 2013
