Precision Medicine Approach to Alzheimer's Disease: Rationale and Implications.

The neurodegenerative disease field has enjoyed extremely limited success in the development of effective therapeutics. One potential reason is the lack of disease models that yield accurate predictions and optimal therapeutic targets. Standard clinical trials have pre-determined a single treatment modality, which may be unrelated to the primary drivers of neurodegeneration.

Subtle adversarial image manipulations influence both human and machine perception.

Although artificial neural networks (ANNs) were inspired by the brain, ANNs exhibit a brittleness not generally observed in human perception. One shortcoming of ANNs is their susceptibility to adversarial perturbations-subtle modulations of natural images that result in changes to classification decisions, such as confidently mislabelling an image of an elephant, initially classified correctly, as a clock. In contrast, a human observer might well dismiss the perturbations as an innocuous imaging artifact.

Alzheimer's disease as a systems network disorder: chronic stress/dyshomeostasis, innate immunity, and genetics.

Ineffective results of clinical trials of over 200 anti-Alzheimer's drug candidates, with a 99.6% attrition rate, suggest that the current paradigm of Alzheimer's disease (AD) may be incomplete, necessitating exploration of alternative and complementary frameworks.Using algorithms for hypothesis independent search and expert-assisted synthesis of heterogeneous data, we attempted to reconcile multimodal clinical profiles of early-stage AD patients and accumulated research data within a parsimonious framework.

Dynamic self-guiding analysis of Alzheimer's disease.

We applied a self-guiding evolutionary algorithm to initiate the synthesis of the Alzheimer's disease-related data and literature. A protein interaction network associated with amyloid-beta precursor protein (APP) and a seed model that treats Alzheimer's disease as progressive dysregulation of APP-associated signaling were used as dynamic "guides" and structural "filters" in the recursive search, analysis, and assimilation of data to drive the evolution of the seed model in size, detail, and complexity. Analysis of data and literature across sub-disciplines and system-scale discovery platforms suggests a key role of dynamic cytoskeletal connectivity in the stability, plasticity, and performance of multicellular networks and architectures.