Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning
Abstract Alzheimer’s disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating di...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88134-w |
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| author | ZhanQiang Xie YongLi Situ Li Deng Meng Liang Hang Ding Zhen Guo QinYing Xu Zhu Liang Zheng Shao |
| author_facet | ZhanQiang Xie YongLi Situ Li Deng Meng Liang Hang Ding Zhen Guo QinYing Xu Zhu Liang Zheng Shao |
| author_sort | ZhanQiang Xie |
| collection | DOAJ |
| description | Abstract Alzheimer’s disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. PLCB1, in particular, exhibited the highest diagnostic value in AD and showed significant correlation with Braak stages and neuronal expression. Furthermore, Noscapine, PX-316, and TAK-901 were selected as potential therapeutic drugs for AD based on PLCB1. This research provides a comprehensive and reliable method for the discovery of AD therapeutic targets and the construction of diagnostic models, offering important insights and directions for future AD treatment strategies and drug development. |
| format | Article |
| id | doaj-art-7a4c527da432476aa7a0e43d592ca0b3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7a4c527da432476aa7a0e43d592ca0b32025-02-02T12:15:57ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-025-88134-wIdentification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learningZhanQiang Xie0YongLi Situ1Li Deng2Meng Liang3Hang Ding4Zhen Guo5QinYing Xu6Zhu Liang7Zheng Shao8Department of Thoracic Surgery, Affiliated Hospital of Guangdong Medical UniversityDepartment of Parasitology, Guangdong Medical UniversityDepartment of Parasitology, Guangdong Medical UniversityDepartment of Parasitology, Guangdong Medical UniversityInstitute of Biochemistry and Molecular Biology, Guangdong Medical UniversityLaboratory of Pathogenic Biology, Guangdong Medical UniversityDepartment of Parasitology, Guangdong Medical UniversityDepartment of Thoracic Surgery, Affiliated Hospital of Guangdong Medical UniversityDepartment of Parasitology, Guangdong Medical UniversityAbstract Alzheimer’s disease (AD) is a complex neurodegenerative disorder that currently lacks effective treatment options. This study aimed to identify potential therapeutic targets for the treatment of AD using comprehensive bioinformatics methods and machine learning algorithms. By integrating differential gene expression analysis, weighted gene co-expression network analysis, Mfuzz clustering, single-cell RNA sequencing, and machine learning algorithms including LASSO regression, SVM-RFE, and random forest, five hub genes related to AD, including PLCB1, NDUFAB1, KRAS, ATP2A2, and CALM3 were identified. PLCB1, in particular, exhibited the highest diagnostic value in AD and showed significant correlation with Braak stages and neuronal expression. Furthermore, Noscapine, PX-316, and TAK-901 were selected as potential therapeutic drugs for AD based on PLCB1. This research provides a comprehensive and reliable method for the discovery of AD therapeutic targets and the construction of diagnostic models, offering important insights and directions for future AD treatment strategies and drug development.https://doi.org/10.1038/s41598-025-88134-wAlzheimer’s diseaseWGCNAMfuzzscRNA-seqPLCB1Neurons |
| spellingShingle | ZhanQiang Xie YongLi Situ Li Deng Meng Liang Hang Ding Zhen Guo QinYing Xu Zhu Liang Zheng Shao Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning Scientific Reports Alzheimer’s disease WGCNA Mfuzz scRNA-seq PLCB1 Neurons |
| title | Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning |
| title_full | Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning |
| title_fullStr | Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning |
| title_full_unstemmed | Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning |
| title_short | Identification of therapeutic targets for Alzheimer’s Disease Treatment using bioinformatics and machine learning |
| title_sort | identification of therapeutic targets for alzheimer s disease treatment using bioinformatics and machine learning |
| topic | Alzheimer’s disease WGCNA Mfuzz scRNA-seq PLCB1 Neurons |
| url | https://doi.org/10.1038/s41598-025-88134-w |
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