Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease
(1) Background: Parkinson’s disease (PD) is a progressively worsening neurodegenerative disorder affecting movement, mental well-being, sleep, and pain. While no cure exists, treatments like hyperbaric oxygen therapy (HBOT) offer potential relief. However, the molecular biology perspective, especial...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/4/1/9 |
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| author | Eirini Banou Aristidis G. Vrahatis Marios G. Krokidis Panagiotis Vlamos |
| author_facet | Eirini Banou Aristidis G. Vrahatis Marios G. Krokidis Panagiotis Vlamos |
| author_sort | Eirini Banou |
| collection | DOAJ |
| description | (1) Background: Parkinson’s disease (PD) is a progressively worsening neurodegenerative disorder affecting movement, mental well-being, sleep, and pain. While no cure exists, treatments like hyperbaric oxygen therapy (HBOT) offer potential relief. However, the molecular biology perspective, especially when intertwined with machine learning dynamics, remains underexplored. (2) Methods: We employed machine learning techniques to analyze single-cell RNA-seq data from human PD cell samples. This approach aimed to identify pivotal genes associated with PD and understand their relationship with HBOT. (3) Results: Our analysis indicated genes such as MAP2, CAP2, and WSB1, among others, as being crucially linked with Parkinson’s disease (PD) and showed their significant correlation with Hyperbaric oxygen therapy (HBOT) indicatively. This suggests that certain genomic factors might influence the efficacy of HBOT in PD treatment. (4) Conclusions: HBOT presents promising therapeutic potential for Parkinson’s disease, with certain genomic factors playing a pivotal role in its efficacy. Our findings emphasize the need for further machine learning-driven research harnessing diverse omics data to better understand and treat PD. |
| first_indexed | 2024-04-24T18:31:41Z |
| format | Article |
| id | doaj.art-d62c610805d841dea320ea41041eed9b |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-04-24T18:31:41Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-d62c610805d841dea320ea41041eed9b2024-03-27T13:27:28ZengMDPI AGBioMedInformatics2673-74262024-01-014112713810.3390/biomedinformatics4010009Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s DiseaseEirini Banou0Aristidis G. Vrahatis1Marios G. Krokidis2Panagiotis Vlamos3Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, GreeceBioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece(1) Background: Parkinson’s disease (PD) is a progressively worsening neurodegenerative disorder affecting movement, mental well-being, sleep, and pain. While no cure exists, treatments like hyperbaric oxygen therapy (HBOT) offer potential relief. However, the molecular biology perspective, especially when intertwined with machine learning dynamics, remains underexplored. (2) Methods: We employed machine learning techniques to analyze single-cell RNA-seq data from human PD cell samples. This approach aimed to identify pivotal genes associated with PD and understand their relationship with HBOT. (3) Results: Our analysis indicated genes such as MAP2, CAP2, and WSB1, among others, as being crucially linked with Parkinson’s disease (PD) and showed their significant correlation with Hyperbaric oxygen therapy (HBOT) indicatively. This suggests that certain genomic factors might influence the efficacy of HBOT in PD treatment. (4) Conclusions: HBOT presents promising therapeutic potential for Parkinson’s disease, with certain genomic factors playing a pivotal role in its efficacy. Our findings emphasize the need for further machine learning-driven research harnessing diverse omics data to better understand and treat PD.https://www.mdpi.com/2673-7426/4/1/9Parkinson’s diseasehyperbaric oxygen therapymachine learninggenomic factorssingle-cell RNA-seq |
| spellingShingle | Eirini Banou Aristidis G. Vrahatis Marios G. Krokidis Panagiotis Vlamos Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease BioMedInformatics Parkinson’s disease hyperbaric oxygen therapy machine learning genomic factors single-cell RNA-seq |
| title | Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease |
| title_full | Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease |
| title_fullStr | Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease |
| title_full_unstemmed | Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease |
| title_short | Machine Learning Analysis of Genomic Factors Influencing Hyperbaric Oxygen Therapy in Parkinson’s Disease |
| title_sort | machine learning analysis of genomic factors influencing hyperbaric oxygen therapy in parkinson s disease |
| topic | Parkinson’s disease hyperbaric oxygen therapy machine learning genomic factors single-cell RNA-seq |
| url | https://www.mdpi.com/2673-7426/4/1/9 |
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