artificial neural networks, with 91.2 percent accuracy; and logistic regression, with 89.2 percent accuracy. Further analysis of prediction models revealed prioritized importance of the prognostic...

artificial neural networks, with 91.2 percent accuracy; and logistic regression, with 89.2 percent accuracy. Further analysis of prediction models revealed prioritized importance of the prognostic factors, which can then be used as basis for further clinical and biological research studies.

These examples (among many others in the medical literature) show that advanced data mining techniques can be used to develop models that possess a high degree of predictive as well as explanatory power. Although data mining methods are capable of extracting patterns and relationships hidden deep in large and complex medical databases, without the cooperation and feedback from the medical experts their results are not of much use. The patterns found via data mining methods should be evaluated by medical professionals who have years of experience in the problem domain to decide whether they are logical, actionable, and novel to warrant new research directions. In short, data mining is not meant to replace medical professionals and researchers, but to complement their invaluable efforts to provide data-driven new research directions and to ultimately save more human lives.

1. How can data mining be used for ultimately curing illnesses like cancer?

2. What do you think are the promises and major challenges for data miners in contributing to medical and biological research endeavors?