Bioinformatics Approaches for Parkinson’s Disease in Clinical Practice: Data-Driven Biomarkers and Pharmacological Treatment

Krokidis, Marios G.; Exarchos, Themis P.; Vlamos, Panayiotis

doi:10.1007/978-3-030-78775-2_23

Cited by 1 publication

(1 citation statement)

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“…Parkinson’s disease (PD) is the second most common chronic progressive neurodegenerative disease after Alzheimer’s disease. It has many causes and clinical manifestations [ 1 , 2 , 3 ], and the incidence of PD is gradually rising with the progress and development of society. The pathological hallmark of PD is the loss of dopaminergic neurons in the substantia nigra pars compacta and the accumulation of α-synuclein-rich intraneuronal aggregates [ 4 , 5 ], resulting in insufficient dopamine release and characteristic motor symptoms.…”

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confidence: 99%

Identification of Potential Parkinson’s Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network

Liu

Peng

et al. 2022

Molecules

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Parkinson’s disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson’s disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision–recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

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