Construction of an Exudative Age-Related Macular Degeneration Diagnostic and Therapeutic Molecular Network Using Multi-Layer Network Analysis, a Fuzzy Logic Model, and Deep Learning Techniques: Are Retinal and Brain Neurodegenerative Disorders Related?
age-related macular degeneration
deep learning
diabetic retinopathy
fuzzy logic
multi-layer network
neurodegenerative disorders
Journal
Pharmaceuticals (Basel, Switzerland)
ISSN: 1424-8247
Titre abrégé: Pharmaceuticals (Basel)
Pays: Switzerland
ID NLM: 101238453
Informations de publication
Date de publication:
02 Nov 2023
02 Nov 2023
Historique:
received:
27
09
2023
revised:
26
10
2023
accepted:
27
10
2023
medline:
25
11
2023
pubmed:
25
11
2023
entrez:
25
11
2023
Statut:
epublish
Résumé
Neovascular age-related macular degeneration (nAMD) is a leading cause of irreversible visual impairment in the elderly. The current management of nAMD is limited and involves regular intravitreal administration of anti-vascular endothelial growth factor (anti-VEGF). However, the effectiveness of these treatments is limited by overlapping and compensatory pathways leading to unresponsiveness to anti-VEGF treatments in a significant portion of nAMD patients. Therefore, a system view of pathways involved in pathophysiology of nAMD will have significant clinical value. The aim of this study was to identify proteins, miRNAs, long non-coding RNAs (lncRNAs), various metabolites, and single-nucleotide polymorphisms (SNPs) with a significant role in the pathogenesis of nAMD. To accomplish this goal, we conducted a multi-layer network analysis, which identified 30 key genes, six miRNAs, and four lncRNAs. We also found three key metabolites that are common with AMD, Alzheimer's disease (AD) and schizophrenia. Moreover, we identified nine key SNPs and their related genes that are common among AMD, AD, schizophrenia, multiple sclerosis (MS), and Parkinson's disease (PD). Thus, our findings suggest that there exists a connection between nAMD and the aforementioned neurodegenerative disorders. In addition, our study also demonstrates the effectiveness of using artificial intelligence, specifically the LSTM network, a fuzzy logic model, and genetic algorithms, to identify important metabolites in complex metabolic pathways to open new avenues for the design and/or repurposing of drugs for nAMD treatment.
Identifiants
pubmed: 38004422
pii: ph16111555
doi: 10.3390/ph16111555
pmc: PMC10674956
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NEI NIH HHS
ID : P30 EY016665
Pays : United States
Organisme : NEI NIH HHS
ID : P30EY016665
Pays : United States
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