Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program. Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology. Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.

Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Several existing and proprietary machine learning frameworks were applied to transcriptomic data. Through a proprietary interactive machine learning system, we were able to uncover several underlying biological mechanisms associated with AD pathology. These results, in turn, informed new hypotheses and identified novel targets for treatment. This paper reviews how the use of explainable machine learning technologies that are capable of extracting insights from small data can potentially provide a new taxonomy of disease. This is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. We share results from such an effort involving a set of AD subject transcriptomic samples where we provide a combinatorial view of how the pathology associated with what we call AD could emerge.

This is a review of technology developed at the labs of NetraMark Corp. The technology advances the promise of precision medicine by providing explainable AI through an augmented intelligence interactive platform. The system described herein has the ability to accurately discover unknown patient sub-types so that a powerful new taxonomy of complex disorders can be discovered. Further, each subtype is clearly explained. This advancement means that clinical trials can be optimized and drug discovery clearly focused around precise etiologies.