Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

Lessons from complexity science for urban health and well-being

From a complexity science perspective, urban health and well-being challenges emerge due to the complexity of urban systems. Adverse urban health outcomes emerge from failing to respond to that complexity by taking a systems approach in knowledge and action which would open opportunity spaces for human agents to create benefits which in turn would generate salutogenic health and well-being outcomes. Lessons learned from complexity science suggest that adverse urban health outcomes emerge from a poor understanding of their complexity and from not engaging with them in a transdisciplinary, integrated fashion. A conceptual framework is presented which combines systems models from the natural and social sciences and explains how opportunities for advancing health and well-being can be co-created. The framework demonstrates that taking a systems approach is a necessary cognitive response from learning the lessons of complexity science and from understanding that humans are an inextricable part of the systems they aim at understanding and managing. Such response is at the core of systems intelligence. The implications are far reaching for the science of urban health and well-being