Innovative machine-learning approach for future diagnostic advances in Parkinson’s disease
Excerpt from the Article:
Parkinson’s disease (PD) is the second most common neurodegenerative disease, with patient numbers being expected to double worldwide in the next 20 years. The detailed molecular and cellular mechanisms underlying its pathogenesis remains unclear, although recent evidence has pointed towards the role of mitochondrial dysfunction in the onset of the disease. Mitochondria — small cellular ‘subunits’ involved in cell metabolism and energy generation — constantly and dynamically interact with each other, forming perpetually changing networks known as mitochondria interaction networks (MINs). The researchers therefore sought to understand the correlation between the mitochondrial impairments observed in PD and any specific network topological changes in MINs, with the aim of advancing the early diagnosis and classification of PD patients.
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