Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms.
Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms.
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DOI：
10.1016/j.isci.2022.103910
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发表时间：
2022-03-18
期刊：
影响因子：
5.8
通讯作者：
Guan Y
中科院分区：
文献类型：
--
作者：
Zhang H;Guo J;Li H;Guan Y
在一线疟疾治疗、青蒿素类复方疗法方面，抗药性正在迅速演变。这一直是一个悬而未决的问题，这种耐药性状态的预测模型是否可以在体内-体外转录组学测量中推广。在这项研究中，我们提出了一个模型，预测青蒿素治疗耐药性恶性疟原虫的转录组信息。我们证明了该模型在体内清除率和体外IC 50测量方面以及基于不同的微阵列和数据处理模式的稳健性。该算法的有效性进一步得到了其在DREAM疟疾挑战中的第一个位置的支持。我们鉴定了青蒿素治疗抗性的转录生物标志物，其可以预测青蒿素抗性并且在其表达模块中是保守的。这是疟疾治疗研究的关键一步，因为它展示了利用分子生物标志物建立一个平台强大的个性化青蒿素耐药性模型的潜力。青蒿素抗性可以通过机器学习从转录组中预测我们的模型可以在体内和体外以及不同平台之间转移我们确定了青蒿素抗性的顶级转录生物标志物制药科学;微生物寄生虫;生物信息学;人工智能
Drug resistance has been rapidly evolving with regard to the first-line malaria treatment, artemisinin-based combination therapies. It has been an open question whether predictive models for this drug resistance status can be generalized across in vivo-in vitro transcriptomic measurements. In this study, we present a model that predicts artemisinin treatment resistance developed with transcriptomic information of Plasmodium falciparum. We demonstrated the robustness of this model across in vivo clearance rate and in vitro IC50 measurement and based on different microarray and data processing modalities. The validity of the algorithm is further supported by its first placement in the DREAM Malaria challenge. We identified transcription biomarkers to artemisinin treatment resistance that can predict artemisinin resistance and are conserved in their expression modules. This is a critical step in the research of malaria treatment, as it demonstrated the potential of a platform-robust, personalized model for artemisinin resistance using molecular biomarkers. Artemisinin resistance can be predicted from transcriptomes by machine learning Our model can be transferred between in vivo and in vitro and different platforms We identified top transcription biomarkers of artemisinin resistance Pharmaceutical science; Microbiology parasite; Bioinformatics; Artificial intelligence
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影响因子：
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作者：
Ford, Colby T;Janies, Daniel
通讯作者：
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