Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis Methods Article Swipe
Jiye Wang
,
Lin Luo
,
Qiong Ding
,
Zengrui Wu
,
Yayuan Peng
,
Jie Li
,
Xiaoqin Wang
,
Weihua Li
,
Guixia Liu
,
Bo Zhang
,
Yun Tang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2021.754175
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2021.754175
Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo. Here we proposed a systematic framework to discover potential therapeutic targets, and further explore the underlying mechanism of kaempferide, one of major ingredients from Vernonia anthelmintica (L.) willd , for vitiligo. By collecting transcriptome and protein-protein interactome data, the combination of random forest (RF) and greedy articulation points removal (GAPR) methods was used to discover potential therapeutic targets for vitiligo. The results showed that the RF model performed well with AUC (area under the receiver operating characteristic curve) = 0.926, and led to prioritization of 722 important transcriptomic features. Then, network analysis revealed that 44 articulation proteins in vitiligo network were considered as potential therapeutic targets by the GAPR method. Finally, through integrating the above results and proteomic profiling of kaempferide, the multi-target strategy for vitiligo was dissected, including 1) the suppression of the p38 MAPK signaling pathway by inhibiting CDK1 and PBK, and 2) the modulation of cellular redox homeostasis, especially the TXN and GSH antioxidant systems, for the purpose of melanogenesis. Meanwhile, this strategy may offer a novel perspective to discover drug candidates for vitiligo. Thus, the framework would be a useful tool to discover potential therapeutic strategies and drug candidates for complex diseases.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphar.2021.754175
- OA Status
- gold
- Cited By
- 16
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3201347190
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3201347190Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fphar.2021.754175Digital Object Identifier
- Title
-
Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis MethodsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-15Full publication date if available
- Authors
-
Jiye Wang, Lin Luo, Qiong Ding, Zengrui Wu, Yayuan Peng, Jie Li, Xiaoqin Wang, Weihua Li, Guixia Liu, Bo Zhang, Yun TangList of authors in order
- Landing page
-
https://doi.org/10.3389/fphar.2021.754175Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fphar.2021.754175Direct OA link when available
- Concepts
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Vitiligo, Computer science, Machine learning, Artificial intelligence, Medicine, ImmunologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
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2025: 5, 2024: 3, 2023: 5, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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73Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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