ArticleScientific reports2022
Predictive modelling of Parkinson's disease progression based on RNA-Sequence with densely connected deep recurrent neural networks.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Predicting depressive symptoms among Chinese college students using recurrent neural networks with longitudinal data.Scientific reports · 2026Article
- Utilisation of Machine Learning Approaches Improves RNA-Seq Transcriptome Analyses in Alzheimer's Disease Brain.Journal of molecular neuroscience : MN · 2026Article
- An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson's disease motor progression.Frontiers in digital health · 2026Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of recent high throughput sequencing technologies resulted in unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson's disease (PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient's next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients without revealing any race, sex, or age information with a variable number of visits and 34,682 predictor variables for 4 years. We propose a predictive model based on deep Recurrent Neural Network (RNN) with the addition of dense connections and batch normalization into RNN layers. The results show that the proposed architecture can predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and a rank-order correlation of (r = 0.83, p < 0.0001) between the predicted and actual disease status of PD.
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