R. Sepulveda, C. St. George, and R.A. Rogers
White Anvil Innovations, LLC,
Keywords: Big Data, unbased computational tool, personal medicine, database
Summary:We have developed computational process that queries databases to minimize or eliminate inherent uncertainty using mathematically unbiased functional analysis. It is applicable to interaction analysis, massive data gathering, and massive data integration using an enriched reference data map useful to identify critical factors for the development of a condition of interest contained within big data. Stepwise progression of various combinations of processes and mathematical equations calculate risk, for example, associated with candidate genes and gene products to compute total risk for an individual. Information derived from networks interactions at the steepest rate of change of all known features are utilized. Information contained in the manner in which these features interact with each other is treated using a series of quantitative metrics, based on graph theory and mathematics, to calculate the risk for developing a particular disease associated with the candidate genes or gene products. Critical biomolecular interactions relevant to discriminate health and disease at the individual level are identified. This computational process identifies the risk of an individual to develop a given physiological or pathological condition (eg. cardiovascular disease) triggered by changes or abnormalities in multiple biochemical elements, such as DNA, single nucleotide polymorphisms, proteins, metabolic processes, etc.