POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917949973987328 |
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| author | He, Zihao Liu, Liu Han, Dongchen Gao, Kai Dong, Lei Bu, Dechao Huo, Peipei Wang, Zhihao Deng, Wenxin Liu, Jingjia Guo, Jin-cheng Zhao, Yi Wu, Yang |
| author_facet | He, Zihao Liu, Liu Han, Dongchen Gao, Kai Dong, Lei Bu, Dechao Huo, Peipei Wang, Zhihao Deng, Wenxin Liu, Jingjia Guo, Jin-cheng Zhao, Yi Wu, Yang |
| contents | Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction under diverse conditions, requiring integration into NP analyses. We developed POINT, a novel NP platform enhanced by multi-omics biological networks, advanced algorithms, and knowledge graphs (KGs) featuring network-based and KG-based analytical functions. In the network-based analysis, users can perform NP studies flexibly using 1,158 multi-omics biological networks encompassing proteins, transcription factors, and non-coding RNAs across diverse cell line-, tissue- and disease-specific conditions. Network-based analysis-including random walk with restart (RWR), GSEA, and diffusion profile (DP) similarity algorithms-supports tasks such as target prediction, functional enrichment, and drug screening. We merged networks from experimental sources to generate a pre-integrated multi-layer human network for evaluation. RWR demonstrated superior performance with a 33.1% average ranking improvement over the second-best algorithm, PageRank, in identifying known targets across 2,002 drugs. Additionally, multi-layer networks significantly improve the ability to identify FDA-approved drug-disease pairs compared to the single-layer network. For KG-based analysis, we compiled three high-quality KGs to construct POINT KG, which cross-references over 90% of network-based predictions. We illustrated the platform's capabilities through two case studies. POINT bridges the gap between multi-omics networks and drug discovery; it is freely accessible at http://point.gene.ac/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs He, Zihao Liu, Liu Han, Dongchen Gao, Kai Dong, Lei Bu, Dechao Huo, Peipei Wang, Zhihao Deng, Wenxin Liu, Jingjia Guo, Jin-cheng Zhao, Yi Wu, Yang Molecular Networks Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction under diverse conditions, requiring integration into NP analyses. We developed POINT, a novel NP platform enhanced by multi-omics biological networks, advanced algorithms, and knowledge graphs (KGs) featuring network-based and KG-based analytical functions. In the network-based analysis, users can perform NP studies flexibly using 1,158 multi-omics biological networks encompassing proteins, transcription factors, and non-coding RNAs across diverse cell line-, tissue- and disease-specific conditions. Network-based analysis-including random walk with restart (RWR), GSEA, and diffusion profile (DP) similarity algorithms-supports tasks such as target prediction, functional enrichment, and drug screening. We merged networks from experimental sources to generate a pre-integrated multi-layer human network for evaluation. RWR demonstrated superior performance with a 33.1% average ranking improvement over the second-best algorithm, PageRank, in identifying known targets across 2,002 drugs. Additionally, multi-layer networks significantly improve the ability to identify FDA-approved drug-disease pairs compared to the single-layer network. For KG-based analysis, we compiled three high-quality KGs to construct POINT KG, which cross-references over 90% of network-based predictions. We illustrated the platform's capabilities through two case studies. POINT bridges the gap between multi-omics networks and drug discovery; it is freely accessible at http://point.gene.ac/. |
| title | POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2503.07203 |