Abstract
Cancer treatment decisions should be based on all available evidence. But this evidence is complex and varied: it includes not only the patient’s symptoms and expert knowledge of the relevant causal processes, but also clinical databases relating to past patients, databases of observations made at the molecular level, and evidence encapsulated in scientific papers and medical informatics systems. Objective Bayesian nets offer a principled path to knowledge integration, and we show in this chapter how they can be applied to integrate various kinds of evidence in the cancer domain. This is important from the systems biology perspective, which needs to integrate data that concern different levels of analysis, and is also important from the point of view of medical informatics.
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References
Abramovitz, M., Leyland-Jones, B.: A systems approach to clinical oncology: Focus on breast cancer. BMC Proteome Science 4, 5 (2006)
Al-Kuraya, K., Schraml, P., Torhorst, J., Tapia, C., Zaharieva, B., Novotny, H., Spichtin, H., Maurer, R., Mirlacher, M., Kochl, O., Zuber, M., Dieterich H., Mross, F., Wilber, K., Simon, R., Sauter, G.: Prognostic relevance of gene amplifications and coamplifications in breast cancer. Cancer Research 64, 8534–8540 (2004)
Alves, R., Antunes, F., Salvador, A.: Tools for kinetic modeling of biochemical networks. Nature Biotechnology 24, 667–672 (2006)
Amgoud, L., Cayrol, C., Lagasquie-Schiex, M.-C.: On bipolarity in argumentation frameworks. In: NMR, pp. 1–9 (2004)
Arroyo-Figueroa, G., Sucar, L.: Temporal Bayesian network of events for diagnosis and prediction in dynamic domains. Applied Intelligence 23, 77–86 (2005)
Bangsø, O., Olesen, K.: Applying object oriented Bayesian networks to large (medical) decision support systems. In: Proceedings of the Eighth Scandinavian Conference on Artificial Intelligence. IOS Press, Amsterdam (2003)
Baudis, M., Cleary, M.: Progenetix.net: an online repository for molecular cytogenetic aberration data. Bioinformatics 17, 1228–1229 (2001)
Borak, J., Veilleux, S.: Errors of intuitive logic among physicians. Soc. Sci. Med. 16, 1939–1947 (1982)
Bulashevska, S., Szakacs, O., Brors, B., Eils, R., Kovacs, G.: Pathways of urothelial cancer progression suggested by Bayesian network analysis of allelotyping data. International Journal of Cancer 110, 850–856 (2004)
Cristofanilli, M., Hayes, D., Budd, G., Ellis, M., Stopeck, A., Reuben, J., Doyle, G., Matera, J., Allard, W., Miller, M., Fritsche, H., Hortobagyi, G., Terstappen, L.: Circulating tumor cells: A novel prognostic factor for newly diagnosed metastatic breast cancer. J. Clin. Oncol. 23, 1420–1430 (2005)
Dawid, A., Mortera, J., Vicard, P.: Object-oriented Bayesian networks for complex forensic DNA profiling problems. Forensic Science International 169(256), 195–205 (2007)
Depew, D., Weber, B.: Darwinism evolving: systems dynamics and the genealogy of natural selection. MIT Press, Cambridge (1996)
Fox, J., Parsons, S.: On using arguments for reasoning about actions and values. In: Proc. AAAI Spring Symposium on Qualitative Preferences in Deliberation and Practical Reasoning, Stanford (1997)
Franklin, B.: Collected Letters, Putnam, New York (1887)
Fridlyand, J., Snijders, A., Ylstra, B., Li, H., Olshen, A., Segraves, R., Dairkee, S., Tokuyasu, T., Ljung, B., Jain, A., McLennan, J., Ziegler, J., Chin, K., Devries, S., Feiler, H., Gray, J., Waldman, F., Pinkel, D., Albertson, D.: Breast tumor copy number aberration phenotypes and genomic instability. BMC Cancer 6, 96 (2006)
Galea, M., Blamey, R., Elston, C., Ellis, I.: The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Research and Treatment 3, 207–219 (1992)
Gard, R.: Buddhism. George Braziller, Inc., New York (1961)
Holland, J.: Hidden order: how adaptation builds complexity. Helix Books, New York (1995)
Holland, J.: Emergence: from chaos to order. Addison-Wesley, Redwood City (1998)
Hunter, A., Besnard, P.: A logic-based theory of deductive arguments. Artificial Intelligence 128, 203–235 (2001)
Jaynes, E.T.: Information theory and statistical mechanics. The Physical Review 106(4), 620–630 (1957)
Kahneman, D., Tversky, A.: On the psychology of prediction. Psychol. Rev. 80, 237–251 (1973)
Khalil, I., Hill, C.: Systems biology for cancer. Curr. Opin. Oncol. 17, 44–48 (2005)
Kitano, H.: Biological robustness. Nat. Rev. Genet. 5, 826–837 (2004)
Koller, D., Pfeffer, A.: Object-oriented Bayesian networks. In: Geiger, D., Shenoy, P. (eds.) Proceedings of the 13th Annual Conference on Uncertainty in Atificial Intelligence, pp. 302–313. Morgan Kaufmann Publishers, San Francisco (1997)
Korb, K.B., Nicholson, A.E.: Bayesian artificial intelligence. Chapman and Hall/CRC Press, London (2003)
Krause, P., Ambler, S., Elvang-Goranssan, M., Fox, J.: A logic of argumentation for reasoning under uncertainty. Computational Intelligence 11, 113–131 (1995)
Laskey, K., Mahoney, S.: Network fragments: Representing knowledge for constructing probabilistic models. In: Geiger, D., Shenoy, P. (eds.) Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence, pp. 334–341. Morgan Kaufmann Publishers, San Francisco (1997)
Lupski, J., Stankiewicz, P.: Genomic disorders: The genomic basis of disease. Humana Press, Totowa (2006)
Mao, B., Wu, W., Davidson, G., Marhold, J., Li, M., Mechler, B., Delius, H., Hoppe, D., Stannek, P., Walter, C., Glinka, A., Niehrs, C.: Kremen proteins are Dickkopf receptors that regulate Wnt/beta-catenin signalling. Nature 417, 664–667 (2002)
McPherson, K., Steel, C., Dixon, J.: Breast cancer: Epidemiology, risk factors and genetics. BMJ 321, 624–628 (2000)
Michielsa, S., Koscielnya, S., Hill, C.: Prediction of cancer outcome with microarrays: a multiple random validation strategy. The Lancet 365(9458), 488–492 (2005)
Mitchell, S.: Biological complexity and integrative pluralism. Cambrige University Press, Cambridge (2003)
Nagl, S.: Objective Bayesian approaches to biological complexity in cancer. In: Williamson, J. (eds.) Proceedings of the Second Workshop on Combining Probability and Logic. (2005) http://www.kent.ac.uk/sec1/philosophy/jw/2005/progic/
Nagl, S.: A path to knowledge: from data to complex systems models of cancer. In: Nagl, S. (ed.) Cancer Bioinformatics, pp. 3–27. John Wiley & Sons, London (2006)
Nagl, S., Williams, M., El-Mehidi, N., Patkar, V., Williamson, J.: Objective Bayesian nets for integrating cancer knowledge: a systems biology approach. In: Rouso, J., Kaski, S., Ukkonen, E. (eds.) Proceedings of the Workshop on Probabilistic Modelling and Machine Learning in Structural and Systems Biology, Tuusula, June 17–18 2006, vol. B-2006-4, pp. 44–49. Helsinki University Printing House, Finland (2006)
Neapolitan, R.E.: Probabilistic reasoning in expert systems: theory and algorithms. Wiley, New York (1990)
Neapolitan, R.E.: Learning Bayesian networks. Pearson/Prentice Hall, Upper Saddle River (2003)
Nygren, P., Larsson, R.: Overview of the clinical efficacy of investigational anticancer drugs. Journal of Internal Medicine 253, 46–75 (2003)
Oyama, S.: The ontogeny of information: developmental systems and evolution, 2nd edn. Duke University Press, Durham (2000)
Parsons, S.: Order of magnitude reasoning and qualitative probability. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11(3), 373–390 (2003)
Parsons, S.: On precise and correct qualitative probabilistic reasoning. International Journal of Approximate Reasoning 35, 111–135 (2004)
Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. In: Schobbens, P.-Y. (ed.) Working Notes of 3rd Model Age Workshop: Formal Models of Agents, Sesimbra, Portugal (1996)
Quinn, M., Allen, E.: Changes in incidence of and mortality from breast cancer in England and Wales since introduction of screening. BMJ 311, 1391–1395 (1995)
Rasnick, D., Duesberg, P.: How aneuploidy affects metabolic control and causes cancer. Biochemical Journal 340, 621–630 (1999)
Ravdin, Siminoff, Davis.: A computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J. Clin. Oncol. 19, 980–991 (2001)
Reis-Filho, J., Simpson, P., Gale, T., Lakhan, S.: The molecular genetics of breast cancer: the contribution of comparative genomic hybridization. Pathol. Res. Pract. 201, 713–725 (2005)
Richards, M., Smith, I., Dixon, J.: Role of systemic treatment for primary operable breast cancer. BMJ 309, 1263–1366 (1994)
Ries, L., Eisner, M., Kosary, C., Hankey, B., Miller, B., Clegg, L., Mariotto, A., Feuer, E., Edwards, B.: SEER Cancer Statistics Review 1975–2001. National Cancer Institute (2004)
Russo, F., Williamson, J.: Interpreting probability in causal models for cancer. In: Russo, F., Williamson, J. (eds.) Causality and probability in the sciences. Texts in Philosophy, pp. 217–241. College Publications, London (2007)
Toyoda, T., Wada, A.: ‘omic space’: coordinate-based integration and analysis of genomic phenomic interactions. Bioinformatics 20, 1759–1765 (2004)
Veer, L., Paik, S., Hayes, D.: Gene expression profiling of breast cancer: a new tumor marker. J. Clin. Oncol. 23, 1631–1635 (2005)
Vogelstein, B., Kinzler, K.: Cancer genes and the pathways they control. Nature Medicine 10, 789–799 (2004)
Williams, M., Williamson, J.: Combining argumentation and Bayesian nets for breast cancer prognosis. Journal of Logic, Language and Information 15, 155–178 (2006)
Williamson, J.: Maximising entropy efficiently. Electronic Transactions in Artificial Intelligence Journal, 6 (2002), http://www.etaij.org
Williamson, J.: Bayesian nets and causality: philosophical and computational foundations. Oxford University Press, Oxford (2005a)
Williamson, J.: Objective Bayesian nets. In: Artemov, S., Barringer, H., ďAvila Garcez, A.S., Lamb, L.C., Woods, J. (eds.) We Will Show Them! Essays in Honour of Dov Gabbay, vol. 2, pp. 713–730. College Publications, London (2005b)
Williamson, J.: Causality. In: Gabbay, D., Guenthner, F. (eds.) Handbook of Philosophical Logic, vol. 14, pp. 89–120. Springer, Heidelberg (2007a)
Williamson, J.: Motivating objective Bayesianism: from empirical constraints to objective probabilities. In: Harper, W.L., Wheeler, G.R. (eds.) Probability and Inference: Essays in Honour of Henry E. Kyburg Jr., pp. 151–179. College Publications, London (2007b)
Williamson, J., Gabbay, D.: Recursive causality in Bayesian networks and self-fibring networks. In: Gillies, D. (ed.) Laws and models in the sciences, pp. 173–221. With comments, pp. 223–245. King’s College Publications, London (2005)
Xia, Y., Yu, H., Jansen, R., Seringhaus, M., Baxter, S., Greenbaum, D., Zhao, H., Gerstein, M.: Analyzing cellular biochemistry in terms of molecular networks. Annu. Rev. Biochem. 73, 1051–1087 (2004)
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Nagl, S., Williams, M., Williamson, J. (2008). Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer. In: Holmes, D.E., Jain, L.C. (eds) Innovations in Bayesian Networks. Studies in Computational Intelligence, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85066-3_6
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