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 mo...

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Bibliographic Details
Main Authors:	Bandres-Ciga, S. (Author), Blauwendraat, C. (Author), Bookman, M. (Author), Botia, J.A (Author), Campbell, R.H (Author), Carter, J.F (Author), Craig, D.W (Author), Dadu, A. (Author), Faghri, F. (Author), Hardy, J.A (Author), Hashemi, S.H (Author), Hutchins, E. (Author), Iwaki, H. (Author), Kim, J.J (Author), Leonard, H.L (Author), Makarious, M.B (Author), Maleknia, M. (Author), Morris, H.R (Author), Nalls, M.A (Author), Nojopranoto, W. (Author), Saffo, D. (Author), Sargent, L. (Author), Singleton, A.B (Author), Song, Y. (Author), Van Keuren-Jensen, K. (Author), Violich, I. (Author), Vitale, D. (Author)
Format:	Article
Language:	English
Published:	Nature Research 2022
Subjects:	accuracy algorithm area under the curve Article binocular convergence biobank biological marker discriminant analysis DNA sequence DNA sequencing gene gene expression gene interaction genetic transcription human machine learning Parkinson disease prediction predictive model predictive value quality control RNA sequencing sensitivity and specificity single nucleotide polymorphism support vector machine training
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Multi-modality machine learning predicting Parkinson’s disease

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