readers can check our referenced publications and references therein [11–13]. To guarantee the reproduction of all our experiments, we use algorithms from the Weka Package [3] as classifiers. All the classifiers were used with the default parameters; we are convinced that better results could be found if adjustments are made in each classifier (considering only its result over the training set). The first signature we uncovered contains 10 proteins, see Table 4. Using the Pathway Studio software [3], we generated an undirected graph of the known ‘direct relations’ of these 10 proteins. Each node in the graph corresponds to a protein and an edge exists if the Pathway Studio software produced a ‘direct relation’, indicating important association already observed in the life sciences literature. On this graph we looked for its maximum clique (Fig. 3a). We denote this graph as G = (V,E). Each vertex in V has a one-to-one correspondence with a protein. Each pair of vertices are connected by an edge in E, if and only if, there are many direct relations between the proteins reported in the literature. A clique in G is a subset X of V such that its induced graph G[X] is complete. In other words, we are looking for the maximum subset of proteins, in which all pairs of proteins already have a direct relationship identified between them, thus we consider this set the core of our 10-protein signature (this core has the 6-proteins listed above, see Fig. 3b). Our first benchmark test for this 6-protein signature was done using Simple Logistic (SL) [14], perhaps the simplest classifier from References 1. Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, et al. (2007) Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nat Med 13: 1359–1362. 2. Witten IH, Frank E (2005) Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann. 3. Ariadne Genomics I (2007) Pathway StudioTM. 5.0 ed. 4. Bruunsgaard H, Andersen-Ranberg K, Jeune B, Pedersen AN, Skinhoj P, et al. (1999) A high plasma concentration of TNF-alpha is associated with dementia in centenarians. J Gerontol A Biol Sci Med Sci 54: M357–364. 5. Finch CE, Morgan TE (2007) Systemic Inflammation, Infection, ApoE Alleles, and Alzheimer Disease: A Position Paper. Current Alzheimer Research 4: 185–189. 6. Magaki S, Mueller C, Dickson C, Kirsch W (2007) Increased production of inflammatory cytokines in mild cognitive impairment. Experimental Gerontol- ogy 42: 233–240. 7. Fayyad UM, Irani KB (1993) Multi-Interval Discretization of Continuos-Valued Attributes for Classification Learning. In: Bajcsy R, ed. Morgan Kaufmann. pp 1022–1029. Alzheimer the Weka software suite. With our 6-protein signature, SL had a performance of 86% after applying 10 times 10-fold cross- validation over the training set (Fig. 3c). When considering the ‘‘AD’’ test set, our 6-protein signature with SL was able to make a classification with 97% of accuracy. For AD samples we achieved 100% positive agreement and for NDC samples a 92% negative agreement (Fig. 3d). When using the second test set (labelled ‘‘MCI’’), that includes samples that had an initial diagnosis of mild cognitive impairment, the performance of all signatures increases the number of errors. It is reasonable to expect that our very trimmed classifiers are going to have some degradation of performance, as they have not been trained to distinguish confirmed AD samples from those that have MCI. When using the same signature to differentiate between AD and other samples of MCI patients, the occurrence of more errors is an expected outcome (Table 9). In spite of this fact, the overall performance of all signatures seems very robust. Acknowledgments The authors would like thank the editors for their useful comments that have helped to improve the presentation of these results. Author Contributions Conceived and designed the experiments: MGR PM. Performed the experiments: MGR PM. Analyzed the data: MGR PM. Contributed reagents/materials/analysis tools: MGR PM. Wrote the paper: MGR PM. 8. Berretta R, Costa W, Moscato P (2008) Combinatorial Optimization Models for Finding Genetic Signatures from Gene Expression Datasets. In: Keith JM, ed. Bioinformatics, Volume II: Structure, Function and Applications Humana Press. 9. Berretta R, Mendes A, Moscato P (2007) Selection of Discriminative Genes in Microarray Experiments Using Mathematical Programming. Journal of Research and Practice in Information Technology 39: 13. 10. Cotta C, Sloper C, Moscato P (2004) Evolutionary Search of Thresholds for Robust Feature Set Selection: Application to the Analysis of Microarray Data. Applications of Evolutionary Computing. pp 21–30. 11. Cotta C, Langston MA, Moscato P (2007) Combinatorial and Algorithmic Issues for Microarray Analysis. In: Gonzalez TF, ed. Handbook of Approximation Algorithms and Metaheuristics Chapman & Hall/CRC. pp 74.71–74.14. 12. Mendes A, Scott RJ, Moscato P (2008) Microarrays—Identifying Molecular Portraits for Prostate Tumors with Different Gleason Patterns. Clinical Bioinformatics. pp 131–151. 13. Moscato P, Berretta R, Hourani Ma, Mendes A, Cotta C (2005) Genes Related with Alzheimer’s Disease: A Comparison of Evolutionary Search, Statistical and Integer Programming Approaches. Applications on Evolutionary Computing. pp 84–94. 14. Niels L, Mark H, Eibe F (2005) Logistic Model Trees. Mach Learn 59: 161–205. PLoS ONE | www.plosone.org 12 September 2008 | Volume 3 | Issue 9 | e3111