A Non-invasive Detection of Parkinson's Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Bhattacharjee, A.; Jamal, T. B.; Ahammad, I.; Lamisa, A. B.; Arefin, M. S.; Chowdhury, Z. M.; Hossain, M. U.; Das, K. C.; Keya, C. A.; Salimullah, M.

doi:10.1101/2023.10.15.562440

bioRxiv · 10.1101/2023.10.15.562440

A Non-invasive Detection of Parkinson's Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Bhattacharjee, A.·Jamal, T. B.·Ahammad, I.·Lamisa, A. B.·Arefin, M. S.·Chowdhury, Z. M.·Hossain, M. U.·Das, K. C.·Keya, C. A.·Salimullah, M.

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Abstract

Parkinsons disease (PD) is a progressive neurodegenerative disorder affecting the central nervous system, often diagnosed in its advanced stages due to the absence of sensitive biomarkers. With this objective in mind, our study conducted a comprehensive analysis of differentially expressed genes (DEGs) sourced from blood-based microarray datasets to uncover potential biomarkers and developed a machine learning based classifier to conduct two step validations. By analyzing gene expression of three projects, we identified 678 DEGs, consisting of 337 genes showing upregulation and 341 genes presenting downregulation. Additionally, insights from functional enrichment and the protein-protein network analysis indicate that HLA-F, IRF-1, and RPS28 have the potential to serve as biomarkers for diagnosing PD. Simultaneously, we employed feature selection techniques such as Least Absolute Shrinkage and Selection Operator with Cross Validation (LassoCV) followed by Recursive Feature Elimination with Cross Validation (REFCV) to filter our initial dataset of 13,249 genes down to 43 genes, which were subsequently used to train the machine learning-based classifier models. These 43 genes formed the basis for training and testing various machine learning models, including logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, and deep learning based artificial neural networks. Our models demonstrated robust performance, with Support Vector Machine outperforming others by 0.65 accuracy (95%CI: 0.58-0.66), 0.70 AUC-ROC (95%CI: 0.70-0.71) and 0.35 MCC (95%CI: 0.34-0.39). The model was implemented to develop the PitArray tool for non-invasive detection of PD from blood. PitArray is available at: https://github.com/Arittra95/PitArray. Key PointsO_LIHLA-F, IRF-1, and RPS28 were identified as potential biomarkers for Parkinsons disease diagnosis. C_LIO_LISeveral sophisticated feature selection methods recognized 43 genes which were then used to build a machine learning model. C_LIO_LIA Support Vector Machine based tool named PitArray was developed which could distinguish Parkinsons disease patients from healthy people based on blood transcriptome data. C_LI

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Bhattacharjee, A., Jamal, T. B., Ahammad, I., Lamisa, A. B., Arefin, M. S., Chowdhury, Z. M., Hossain, M. U., Das, K. C., Keya, C. A., Salimullah, M.. 2023-10-18. A Non-invasive Detection of Parkinson's Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach. https://doi.org/10.1101/2023.10.15.562440

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