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Dakna, M., Harris, K., Kalousis, A., Carpentier, S., Kolch, W., Schanstra, J. P., Haubitz, M., Vlahou, A., Mischak, H. and Girolami, M. (2010) Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers. BMC Bioinformatics, 11(1), p. 594. (doi: 10.1186/1471-2105-11-594)

Hopcroft, L. E.M. , McBride, M. W. , Harris, K. J., Sampson, A. K., McClure, J. D. , Graham, D. , Young, G., Holyoake, T. L., Girolami, M. A. and Dominiczak, A. F. (2010) Predictive response-relevant clustering of expression data provides insights into disease processes. Nucleic Acids Research, 38(20), pp. 6831-6840. (doi: 10.1093/nar/gkq550)

Good, D. M. et al. (2010) Naturally Occurring Human Urinary Peptides for Use in Diagnosis of Chronic Kidney Disease. Molecular and Cellular Proteomics, 9(11), pp. 2424-2437. (doi: 10.1074/mcp.M110.001917)

Molina, F., Dehmer, M., Perco, P., Graber, A., Girolami, M., Spasovski, G., Schanstra, J. P. and Vlahou, A. (2010) Systems biology: opening new avenues in clinical research. Nephrology Dialysis Transplantation, 25(4), pp. 1015-1018. (doi: 10.1093/ndt/gfq033)

Xu, T. et al. (2010) Inferring signaling pathway topologies from multiple perturbation measurements of specific biochemical species. Science Signaling, 3(113), ra20. (doi: 10.1126/scisignal.2000517)

Rogers, S., Scheltema, R. A., Girolami, M. and Breitling, R. (2009) Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics, 25(4), pp. 512-518. (doi: 10.1093/bioinformatics/btn642)

Rogers, S., Girolami, M., Kolch, W., Waters, K. M., Liu, T., Thrall, B. and Wiley, H. S. (2008) Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics, 24(24), pp. 2894-2900. (doi: 10.1093/bioinformatics/btn553)

Vyshemirsky, V. and Girolami, M. A. (2008) Bayesian ranking of biochemical system models. Bioinformatics, 24(6), pp. 833-839. (doi: 10.1093/bioinformatics/btm607)

Rogers, S., Khanin, R. and Girolami, M. (2007) Bayesian model-based inference of transcription factor activity. BMC Bioinformatics, 8(Suppl), (doi: 10.1186/1471-2105-8-S2-S2)

Rogers, S. and Girolami, M. (2005) A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics, 21, pp. 3131-3137. (doi: 10.1093/bioinformatics/bti487)

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