Current humans, Homo sapiens, are genetically and epigenetically very heterogeneous, and subsequently also biologically and physiologically heterogeneous. Much of this heterogeneity likely arose during evolutionary processes, via various iterations of humanoid lineages, and interbreeding. While advantageous from a species perspective, the heterogeneity of humans poses serious challenges to researchers attempting to understand complex disease processes. While the use of inbred preclinical models makes the research effort more effective at some levels, the findings are often not translatable to the more heterogeneous human populations. This conundrum leads to considerable research activity with inbred preclinical models, but modest progress in understanding many complex human conditions and diseases. This article discusses several of the issues around human heterogeneity and the need to change some directions in preclinical model research. Using newer Artificial Intelligence and Machine Learning approaches can begin to deduce important elements from the complexity of human heterogeneity.
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