5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression

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dc.authoridTemiz, Kubra/0000-0002-3660-3204
dc.contributor.authorTemiz, Kubra
dc.contributor.authorGul, Aytac
dc.contributor.authorGov, Esra
dc.date.accessioned2026-02-27T07:33:18Z
dc.date.available2026-02-27T07:33:18Z
dc.date.issued2025
dc.description.abstractAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-analysis using 4 transcriptome data in ALS patients (including motor neuron and muscle tissue) and healthy controls. Through this analysis, we uncovered common shared differentially expressed genes (DEGs) separately for motor neurons and muscle tissue. Using common DEGs as proxies, we identified two distinct clusters of highly clustered differential co-expressed cluster genes: the 'Muscle Tissue Cluster' for muscle tissue and the 'Motor Neuron Cluster' for motor neurons. We then evaluated the performance of the nodes of these two modules to distinguish between diseased and healthy states with ML algorithms: KNN, SVM, and Random Forest. Furthermore, we performed drug repurposing analysis and text-mining analyses, employing the nodes of clusters as drug targets to identify novel drug candidates for ALS. The potential impact of the drug candidates on the expression of cluster genes was predicted using linear regression, SVR, Random Forest, Gradient Boosting, and neural network algorithms. As a result, we identified five novel drug candidates for the treatment of ALS: Nilotinib, Trovafloxacin, Apratoxin A, Carboplatin, and Clinafloxacin. These findings highlight the potential of drug repurposing in ALS treatment and suggest that further validation through experimental studies could lead to new therapeutic avenues.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye (TUBITAK)
dc.description.sponsorshipOpen access funding provided by the Scientific and Technological Research Council of Turkiye (TUBITAK). This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
dc.identifier.doi10.1007/s12017-025-08847-z
dc.identifier.issn1535-1084
dc.identifier.issn1559-1174
dc.identifier.issue1
dc.identifier.pmid40180646
dc.identifier.urihttp://dx.doi.org/10.1007/s12017-025-08847-z
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4541
dc.identifier.volume27
dc.identifier.wosWOS:001459084300001
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherHumana Press Inc
dc.relation.ispartofNeuromolecular Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectAmyotrophic lateral sclerosis
dc.subjectDifferential co-expression analysis
dc.subjectDrug repurposing
dc.subjectMachine learning
dc.subjectText-mining
dc.title5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression
dc.typeArticle

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