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Predictive modeling in cancer: where systems biology meets the stock market

Forecasting future trends in observable phenomena is of great interest to many disciplines. Methods and models that are able to predict future outcomes have a variety of applications from the prices of stocks within an investment portfolio to the need for adjuvant chemotherapy after radical surgery. These applications may appear vastly different, but each represents a prediction of behavior within a complex system against a background of competing risks and chance events. While the rules that govern the behavior of these systems differ, each can be analyzed and emulated by similar modeling techniques. In general, systems with a limited number of independent parameters that are directly related to future events can be understood relatively simply and are easily replicated. However, real-world problems are multidimensional with interconnected para meters. Furthermore, descriptive datasets are generally small and contain contaminating noise (variations in parameters that are unrelated to the system). Deductive methods can be inadequate for solving problems within such systems, as the rules governing behavior are usually unknown and the systems are extremely complex. In particular, observed data may represent only a fraction of the whole dataset, may not include key recordings and each variable may interact with others making them unsuitable for ana lysis in isolation. Thus, new techniques are required to examine complex systems, such as those required in clinical practice and molecular biology.