用于目标识别的概率知识图谱

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2024-04-01 DOI:10.1371/journal.pcbi.1011945
Chang Liu, Kaimin Xiao, Cuinan Yu, Yipin Lei, Kangbo Lyu, Tingzhong Tian, Dan Zhao, Fengfeng Zhou, Haidong Tang, Jianyang Zeng
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引用次数: 0

摘要

及早发现安全有效的疾病靶点对于降低药物研发项目的巨大成本至关重要。然而,现有的鉴定新靶点的实验方法一般都是劳动密集型的,而且容易失败。另一方面,计算方法,尤其是基于机器学习的框架,已在药物发现中显示出显著的应用潜力。在这项工作中,我们提出了基于机器学习的新型靶点识别框架 Progeni。除了充分利用各种来源的已知异构生物网络外,Progeni 还整合了有关生物实体之间关系的文献证据,构建了一个概率知识图谱。然后,Progeni 利用图神经网络来学习生物实体的特征嵌入,从而促进生物相关候选目标的识别。对 Progeni 的全面评估表明,它在目标识别任务上的预测能力优于基线方法。此外,我们进行的大量测试表明,Progeni 对推荐系统中常见的暴露偏差的负面影响表现出很高的鲁棒性,并能有效识别出得到文献有力支持的新靶点。此外,我们的湿实验室实验成功验证了 Progeni 预测的黑色素瘤和结直肠癌顶级候选靶点的生物学意义。所有这些结果表明,Progeni 可以识别生物学上有效的靶点,从而为推进药物发现过程提供了一个强大而有用的工具。
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A probabilistic knowledge graph for target identification
Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.
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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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