Computational analysis of transcriptome data and mapping of functional networks in Parkinson's disease.

Parkinson's disease (PD) is the most common neurodegenerative movement disorder. The pathophysiology is defined by a loss of dopaminergic neurons in the substantia nigra pars compacta, however recent studies suggest that the peripheral immune system may participate in PD development. Herein, we analyzed molecular insights examining RNA-seq data obtained from the peripheral blood of both Parkinson's disease patients and healthy control. Although all age and gender groups were analyzed, emphasis is given on individuals aged 50-70, the most prevalent group for Parkinson's diagnosis. The computational workflow comprises both bioinformatics analyses and machine learning processes and the yield of the pipeline includes transcripts ranked by their level of significance, which could serve as reliable genetic signatures. Classification outcomes are also examined with a focus on the significance of selected features, ultimately facilitating the development of gene networks implicated in the disease. The thorough functional analysis of the most prominent genes, regarding their biological relevance to PD, indicates that the proposed framework has strong potential for identifying blood-based biomarkers of the disease. Moreover, this approach facilitates the application of machine learning techniques to RNA-seq data from complex disorders, enabling deeper insights into critical biological processes at the molecular level.