Gene expression ranking change based single sample pre-disease state detection
IntroductionTo prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample avai...
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| Format: | Article |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1509769/full |
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| author | Zhenshen Bao Xianbin Li Peng Xu Xiangzhen Zan |
| author_facet | Zhenshen Bao Xianbin Li Peng Xu Xiangzhen Zan |
| author_sort | Zhenshen Bao |
| collection | DOAJ |
| description | IntroductionTo prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample available. The state transition of the biological system is driven by the variation in regulations between genes.MethodsIn this study, we propose a rapid single-sample pre-disease state-identifying method based on the change in gene expression ranking, which can reflect the coordinated shifts between genes, that is, S-PCR. The R codes of S-PCR can be accessed at https://github.com/ZhenshenBao/S-PCR.ResultsThis model-free method is validated by the successful identification of pre-disease state for both simulated and five real datasets. The functional analyses of the pre-disease state-related genes identified by S-PCR also demonstrate the effectiveness of this computational approach. Furthermore, the time efficiency of S-PCR is much better than that of its peers.DiscussionHence, the proposed S-PCR approach holds immense potential for clinical applications in personalized disease diagnosis. |
| format | Article |
| id | doaj-art-48530cc2bb9d470c8a5d6a23d6d15aec |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-48530cc2bb9d470c8a5d6a23d6d15aec2024-12-04T06:46:13ZengFrontiers Media S.A.Frontiers in Genetics1664-80212024-12-011510.3389/fgene.2024.15097691509769Gene expression ranking change based single sample pre-disease state detectionZhenshen Bao0Xianbin Li1Peng Xu2Xiangzhen Zan3School of Information Engineering, Taizhou University, Taizhou, Jiangsu, ChinaSchool of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi, ChinaInstitute of computational science and technology, Guangzhou University, Guangzhou, Guangdong, ChinaSchool of Cultural and Creative Trade, Shenzhen Pengcheng Technician College, Shenzhen, Guangdong, ChinaIntroductionTo prevent disease, it is of great importance to detect the critical point (pre-disease state) when the biological system abruptly transforms from normal to disease state. However, rapid and accurate pre-disease state detection is still a challenge when there is only a single sample available. The state transition of the biological system is driven by the variation in regulations between genes.MethodsIn this study, we propose a rapid single-sample pre-disease state-identifying method based on the change in gene expression ranking, which can reflect the coordinated shifts between genes, that is, S-PCR. The R codes of S-PCR can be accessed at https://github.com/ZhenshenBao/S-PCR.ResultsThis model-free method is validated by the successful identification of pre-disease state for both simulated and five real datasets. The functional analyses of the pre-disease state-related genes identified by S-PCR also demonstrate the effectiveness of this computational approach. Furthermore, the time efficiency of S-PCR is much better than that of its peers.DiscussionHence, the proposed S-PCR approach holds immense potential for clinical applications in personalized disease diagnosis.https://www.frontiersin.org/articles/10.3389/fgene.2024.1509769/fullpre-disease statestate transitionsingle sampleranking changepersonalized disease diagnosis |
| spellingShingle | Zhenshen Bao Xianbin Li Peng Xu Xiangzhen Zan Gene expression ranking change based single sample pre-disease state detection Frontiers in Genetics pre-disease state state transition single sample ranking change personalized disease diagnosis |
| title | Gene expression ranking change based single sample pre-disease state detection |
| title_full | Gene expression ranking change based single sample pre-disease state detection |
| title_fullStr | Gene expression ranking change based single sample pre-disease state detection |
| title_full_unstemmed | Gene expression ranking change based single sample pre-disease state detection |
| title_short | Gene expression ranking change based single sample pre-disease state detection |
| title_sort | gene expression ranking change based single sample pre disease state detection |
| topic | pre-disease state state transition single sample ranking change personalized disease diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1509769/full |
| work_keys_str_mv | AT zhenshenbao geneexpressionrankingchangebasedsinglesampleprediseasestatedetection AT xianbinli geneexpressionrankingchangebasedsinglesampleprediseasestatedetection AT pengxu geneexpressionrankingchangebasedsinglesampleprediseasestatedetection AT xiangzhenzan geneexpressionrankingchangebasedsinglesampleprediseasestatedetection |