It was the best of times; it was the worst of times. It was the season of population medicine; it was the season of personalized medicine—or was it? Charles Dickens’ fictional depiction of late eighteenth century France walked a tense line that masterfully narrated the untenable socioeconomic disparity of its day. He described the forces that inevitably led to the French revolution. Today in the world of medicine we are on the verge of similar untenable tension. Unravelling of the human genome was indeed an epochal event that brought with it the promise of personalized medicine. Thirteen years later, this is a promise that is certainly yet to be fulfi lled. We are unable to bear the massive weight of the unmet promise of personalized medicine. It is therefore little surprise that any glimmers of its potential realization get great attention and scrutiny. One such glimmer is the work of Dr. J. William Harbour and colleagues in which they have correlated gene expression profiles with prognosis in ocular melanoma ⁽¹⁾. They classified ⁽²⁾ tumor gene expression profiles into two classes, those associated with a higher likelihood of metastasis and those associated with a lower likelihood of metastasis. Their clinicallyrelevant result is an example that rekindles hope that our massive investment in unravelling the human genome may yet yield a return. Our team at Quantum Lucid Research Laboratories consists of ophthalmologists, mathematicians, computer scientists, physicists, and engineers, and has a unique appreciation for how such work bridges ostensibly disparate worlds. Data from the human genome project is an example of big data. Other examples of big data include the troves of imaging and clinical laboratory data which we are accumulating in our health centers and other institutions. It is vital that such big data not lay dormant, but instead be translated into better means for diagnosing and treating disease.

Empiricism

Empiricism is the notion that knowledge is discovered through experiment and experience. In the seventeenth century, Galileo Galilei and his contemporaries popularized this millennia-old school of thought and formally introduced it to the Western world. Empiricism did transform and inform the scientific process, and today needs to be revisited in the area of big data. Empiricism is the executive and ideological arm of the Odaibo Big Data Framework. A wholehearted subscription to empiricism is necessary to make headway. We must acknowledge how little we understand and simultaneously realize how relatively little we actually need to understand to make progress. Big data is at odds with the way science has been done for the last couple of centuries. Science had and continues to be done from small labs studying one thing at a time in relative isolation. This type of “cottage science” can and has yielded great rewards. However it is by itself grossly inadequate to help us translate existing data into better treatments and cures for diseases. Cottage science often seeks to know why, while big data often seeks to know what. The two are not mutually exclusive since the what typically gives birth to the why. The 1965 Physics Nobel Laureate Julian Schwinger once said that the bicycle could never have been designed from first principles. In his view, it could never have been designed from knowledge of whys. Instead it required trial and error. It required experimentation—a sequence of whats. In the case of ocular melanoma, no one currently knows exactly why certain gene expression profiles are associated with more aggressive forms of the disease, while other profiles are associated with less aggressive forms. The exact mechanisms are certainly worth investigating. However, we must recognize that the mechanism questions did not exist till the big data analysis gave rise to them. In the Odaibo Big Data Framework, the investigator embraces the role of listener. Here, the investigator consciously allows nature tell its story through the data.