AI2AMP-PD: Accelerating Parkinsons Diagnosis using Multi-omics and Artificial Intelligence

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AI2AMP-PD: Accelerating Parkinson’s Diagnosis using Multi-omics and Artificial Intelligence PROJECT SUMMARY AND ABSTRACT Parkinson’s disease (PD) affects more than 7 million people worldwide, and biomarkers to bolster the therapeutic pipeline are urgently needed. Developing biomarkers for clinical use is a difficult process that requires evaluation of multiple, large cohorts, each adding confidence to the marker. The Accelerating Medicine Partnership in Parkinson’s disease (AMP PD) consortium provides an unparalleled opportunity to rapidly achieve this previously elusive goal. We hypothesize that a powerful, multi-omics classifier powered by standard and advanced machine learning algorithms will accurately identify PD-associated biomarkers at genome scale. Transcripts and genomic classifiers associated with PD will be identified in early-stage, untreated, patients with Dopamine Transporter- neuroimaging-supported diagnosis represented in the PPMI cohort. Transcripts and genomic classifiers will be rigorously replicated in the independent PDBP and BioFIND cohorts. Multi-omics classifiers using both PD- associated transcriptome changes and PD-associated genomic variants will be built with state-of-the-art deep learning techniques (e.g. variational autoencoder). This analysis will powerfully delineate --- for the first time --- the full spectrum of known and novel, coding and noncoding RNAs linked to PD and detectable in circulating blood cells in a harmonized, large-scale data set. It will develop and test highly innovative multi-omics classifiers and provide a generally useful computational framework for large-scale, unbiased PD biomarker discovery.