Inference of immunomic regulatory networks from immunomic microarray data constitutes, after proper validation, computational knowledge discovery. There are subtle epistemological issues involved in using data-driven, computer-based methodology to obtain scientific knowledge. As Karl Popper explained in his classic book The Logic of Scientific Discovery [114] , every scientific theory consists of an initial irrational act of creativity (induction) followed by rigorous logical consequences (deduction) and testing of the initial hypothesis. Where is the initial act of creativity, in other words the scientific hypothesis, in computational knowledge discovery? Computers are clearly not capable of irrational creativity. Can this be considered science? We maintain that the answer is yes [115] . In fact, the initial irrational act of creativity is there; it is involved in all the steps of experiment design, selection of patients/samples, and choice of statistical methodology. Once these are settled, the actual data analysis is purely logic deduction via the machinery of mathematical operations, as prescribed by Popper. In this deductive stage the computer plays a critical role, as it facilitates the application of very complex computational methods. Therefore, scientist (and statistician) bias here is in fact unavoidable, as it is in all scientific disciplines. In particular, the term data-driven is really a misnomer in describing computational knowledge discovery.