Abstract
Personalised medicine strives to identify the right treatment for the right patient at the right time, integrating different types of biological and environmental information.
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Cavalli-Sforza, L.L., Feldman, M.W.: The application of molecular genetic approaches to the study of human evolution. Nat. Genet. 33, 266–275 (2003)
Collins, F.S., Morgan, M., Patrinos, A.: The human genome project: lessons from large-scale biology. Science 300(5167), 186–290 (2003)
Cooper, G.F., et al.: An efficient bayesian method for predicting clinical outcomes from genome-wide data. In: AMIA Annual Symposium Proceedings, pp. 127–131 (2010)
Emmer-Streib, F.: Personalized medicine: has it started yet? A reconstruction of the early history. Front. Genet. 3(313), 1–4 (2013)
Friedman, N., Linial, M., Nachman, I.: Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000)
Ginsburg, G.S., McCarthy, J.J.: Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol. 19(12), 491–496 (2001)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Gygi, S.P., et al.: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999 (1999)
Hamburg, M.A., Collins, F.S.: The path to personalized medicine. New Engl. J. Med. 363, 301–304 (2010)
Ideker, T., et al.: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292(5518), 929–934 (2001)
Koller D., Sahami M.: Toward optimal feature selection. In: Proceedings of the 13th International Conference on Machine Learning (ICML), pp. 284–292 (1996)
Mourad, R., Sinoquet, C., Leray, P.: A hierarchical bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies. BMC Bioinform. 12(16), 1–20 (2011)
Sachs, K., et al.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721), 523–529 (2005)
Sawicki, M.P., et al.: Human genome project. Am. J. Surg. 165(2), 258–264 (1993)
Schadt, E.E., et al.: An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37(7), 710–717 (2005)
Scutari, M., Strimmer, K.: Introduction to graphical modelling. In: Balding, D.J., Stumpf, M., Girolami, M. (eds.) Handbook of Statistical Systems Biology. Wiley, Hoboken (2011)
Waring, J.F., et al.: Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol. Lett. 120(1–3), 359–368 (2001)
Weston, A.D., Hood, L.: Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J. Proteome Res. 3(2), 179–196 (2004)
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Scutari, M. (2015). Personalised Medicine: Taking a New Look at the Patient. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_8
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DOI: https://doi.org/10.1007/978-3-319-28007-3_8
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