Multi-modality machine learning predicting Parkinson’s disease

• Published in: NPJ Parkinson's Disease Vol. 8; no. 1; pp. 35 - 13
• Main Authors: Makarious, Mary B., Leonard, Hampton L., Vitale, Dan, Iwaki, Hirotaka, Sargent, Lana, Dadu, Anant, Violich, Ivo, Hutchins, Elizabeth, Saffo, David, Bandres-Ciga, Sara, Kim, Jonggeol Jeff, Song, Yeajin, Maleknia, Melina, Bookman, Matt, Nojopranoto, Willy, Campbell, Roy H., Hashemi, Sayed Hadi, Botia, Juan A., Carter, John F., Craig, David W., Van Keuren-Jensen, Kendall, Morris, Huw R., Hardy, John A., Blauwendraat, Cornelis, Singleton, Andrew B., Faghri, Faraz, Nalls, Mike A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2413; 631/208/212; 692/499; 692/53/2423; Algorithms; Artificial intelligence; Automation; Biobanks; Biomarkers; Biomedical and Life Sciences; Biomedicine; Datasets; Genomes; Genomics; Machine learning; Neurology; Neurosciences; Parkinson's disease; Preventive medicine; Research centers; Standard deviation
• ISSN: 2373-8057; 2373-8057
• DOI: 10.1038/s41531-022-00288-w

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.