VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions
The progression of a disease associates with changes in genomic activity, but it remains a challenge to screen genetic biomarkers for clinical applications. The disease progression, in dynamic network methods (DNM), can be analogous to an animated film composed of discrete frames, where each frame r...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9145534/ |
| _version_ | 1811274228745371648 |
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| author | Xuemeng Fan Ping Zhu Xu-Qing Tang |
| author_facet | Xuemeng Fan Ping Zhu Xu-Qing Tang |
| author_sort | Xuemeng Fan |
| collection | DOAJ |
| description | The progression of a disease associates with changes in genomic activity, but it remains a challenge to screen genetic biomarkers for clinical applications. The disease progression, in dynamic network methods (DNM), can be analogous to an animated film composed of discrete frames, where each frame represents a temporary state of the time-varying gene-gene interaction network. The major shortage therein is that the transition between two neighboring temporary states was beyond investigation. Here, we develop an updated computational methodology named after VD-analysis. Because single-gene biomarkers were not approved capable of representing a complex biological process, we firstly introduce V-structure — a gene module composed of three genes and two interactions among them — and define it as unit module. We then identify the perturbed pathways that mark the disease progression, followed with the V-structures identified which drive the pathway perturbations. Such driver V-structures can be taken as eligible biomarkers for clinical applications. To test the feasibility of this method, we apply it to a time course dataset of gene expression related to mouse type-II diabetes (T2D). Result indicates that the whole process of T2D is exactly divided into 3 stages and that the driver V-structures inferred for each stage are qualified biomarkers. In summary, our method contributes to the description of dynamic disease progression and the V-structure biomarkers facilitate the treatments of disease. |
| first_indexed | 2024-04-12T23:15:16Z |
| format | Article |
| id | doaj.art-b211d04384c84e82858e235a3ca9f0bd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:15:16Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b211d04384c84e82858e235a3ca9f0bd2022-12-22T03:12:42ZengIEEEIEEE Access2169-35362020-01-01815320215321410.1109/ACCESS.2020.30107839145534VD-Analysis: A Dynamic Network Framework for Analyzing Disease ProgressionsXuemeng Fan0Ping Zhu1Xu-Qing Tang2https://orcid.org/0000-0002-4018-9114School of Science, Jiangnan University, Wuxi, ChinaSchool of Science, Jiangnan University, Wuxi, ChinaSchool of Science, Jiangnan University, Wuxi, ChinaThe progression of a disease associates with changes in genomic activity, but it remains a challenge to screen genetic biomarkers for clinical applications. The disease progression, in dynamic network methods (DNM), can be analogous to an animated film composed of discrete frames, where each frame represents a temporary state of the time-varying gene-gene interaction network. The major shortage therein is that the transition between two neighboring temporary states was beyond investigation. Here, we develop an updated computational methodology named after VD-analysis. Because single-gene biomarkers were not approved capable of representing a complex biological process, we firstly introduce V-structure — a gene module composed of three genes and two interactions among them — and define it as unit module. We then identify the perturbed pathways that mark the disease progression, followed with the V-structures identified which drive the pathway perturbations. Such driver V-structures can be taken as eligible biomarkers for clinical applications. To test the feasibility of this method, we apply it to a time course dataset of gene expression related to mouse type-II diabetes (T2D). Result indicates that the whole process of T2D is exactly divided into 3 stages and that the driver V-structures inferred for each stage are qualified biomarkers. In summary, our method contributes to the description of dynamic disease progression and the V-structure biomarkers facilitate the treatments of disease.https://ieeexplore.ieee.org/document/9145534/BiomarkersV-structuregene-gene interaction networkT2D |
| spellingShingle | Xuemeng Fan Ping Zhu Xu-Qing Tang VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions IEEE Access Biomarkers V-structure gene-gene interaction network T2D |
| title | VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions |
| title_full | VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions |
| title_fullStr | VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions |
| title_full_unstemmed | VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions |
| title_short | VD-Analysis: A Dynamic Network Framework for Analyzing Disease Progressions |
| title_sort | vd analysis a dynamic network framework for analyzing disease progressions |
| topic | Biomarkers V-structure gene-gene interaction network T2D |
| url | https://ieeexplore.ieee.org/document/9145534/ |
| work_keys_str_mv | AT xuemengfan vdanalysisadynamicnetworkframeworkforanalyzingdiseaseprogressions AT pingzhu vdanalysisadynamicnetworkframeworkforanalyzingdiseaseprogressions AT xuqingtang vdanalysisadynamicnetworkframeworkforanalyzingdiseaseprogressions |