Multi-Modality Machine Learning Predicting Parkinsons Disease
Mary B Makarious,
Sara Bandres Ciga,
Jonggeol Jeff Kim,
Roy H Campbell,
Sayed Hadi Hashemi,
Juan A. Botía,
John F Carter,
David W Craig,
Kendall Van Keuren-Jensen,
Huw R Morris,
John A. Hardy,
Andrew B. Singleton,
Mike A Nalls,
Accelerating Medicines Program - Parkinsons Disease (AMP PD),
Global Parkinsons Genetics Program (GP2)
Posted 07 Mar 2021
bioRxiv DOI: 10.1101/2021.03.05.434104
Posted 07 Mar 2021
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multi-modal data is key moving forward. We build upon previous work to deliver multi-modal predictions of Parkinsons Disease (PD). We performed automated ML on multi-modal data from the Parkinsons 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 Parkinsons Disease Biomarker Program (PDBP) dataset. Finally, networks were built to identify gene communities specific to PD. 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 (balanced accuracy) and other metrics. Combining data modalities outperforms the single biomarker paradigm. UPSIT was the largest contributing predictor for the classification of PD. The transcriptomic data was used to construct a network of disease-relevant transcripts. We have built a model using an automated ML pipeline to make improved multi-omic predictions of PD. The model developed improves disease risk prediction, a critical step for better assessment of PD risk. We constructed gene expression networks for the next generation of genomics-derived interventions. Our automated ML approach allows complex predictive models to be reproducible and accessible to the community.
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