Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord
Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. Although genes causing familial cases have been identified, those of sporadic ALS, which occupies the majority of patients, are still elusive. In this study, we adopted machine learning to buil...
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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/s41598-024-81315-z |
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| author | Jack Cheng Bor-Tsang Wu Hsin-Ping Liu Wei-Yong Lin |
| author_facet | Jack Cheng Bor-Tsang Wu Hsin-Ping Liu Wei-Yong Lin |
| author_sort | Jack Cheng |
| collection | DOAJ |
| description | Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. Although genes causing familial cases have been identified, those of sporadic ALS, which occupies the majority of patients, are still elusive. In this study, we adopted machine learning to build binary classifiers based on the New York Genome Center (NYGC) ALS Consortium’s RNA-seq data of the postmortem spinal cord of ALS and non-neurological disease control. The accuracy of the classifiers was greater than 83% and 77% for the training set and the unseen test set, respectively. The classifiers contained 114 genes. Among them, 41 genes have been reported in previous ALS studies, and others are novel in this field. These genes are involved in mitochondrial respiration, lipid metabolism, endosomal trafficking, and iron metabolism, which may promote the progression of ALS pathology. |
| format | Article |
| id | doaj-art-f0ff74c4cc81454fa794d55eaa920f23 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f0ff74c4cc81454fa794d55eaa920f232025-01-12T12:17:14ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-024-81315-zMachine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cordJack Cheng0Bor-Tsang Wu1Hsin-Ping Liu2Wei-Yong Lin3Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical UniversityDepartment of Senior Citizen Service Management, National Taichung University of Science and TechnologyGraduate Institute of Acupuncture Science, College of Chinese Medicine, China Medical UniversityGraduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical UniversityAbstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. Although genes causing familial cases have been identified, those of sporadic ALS, which occupies the majority of patients, are still elusive. In this study, we adopted machine learning to build binary classifiers based on the New York Genome Center (NYGC) ALS Consortium’s RNA-seq data of the postmortem spinal cord of ALS and non-neurological disease control. The accuracy of the classifiers was greater than 83% and 77% for the training set and the unseen test set, respectively. The classifiers contained 114 genes. Among them, 41 genes have been reported in previous ALS studies, and others are novel in this field. These genes are involved in mitochondrial respiration, lipid metabolism, endosomal trafficking, and iron metabolism, which may promote the progression of ALS pathology.https://doi.org/10.1038/s41598-024-81315-zALSSpinal cordMachine learningRNA-seq |
| spellingShingle | Jack Cheng Bor-Tsang Wu Hsin-Ping Liu Wei-Yong Lin Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord Scientific Reports ALS Spinal cord Machine learning RNA-seq |
| title | Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord |
| title_full | Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord |
| title_fullStr | Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord |
| title_full_unstemmed | Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord |
| title_short | Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord |
| title_sort | machine learning identified novel players in lipid metabolism endosomal trafficking and iron metabolism of the als spinal cord |
| topic | ALS Spinal cord Machine learning RNA-seq |
| url | https://doi.org/10.1038/s41598-024-81315-z |
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