Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria.
Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria.
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DOI：
10.12688/f1000research.21539.1
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发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Janies, Daniel
中科院分区：
文献类型：
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作者：
Ford, Colby T;Janies, Daniel
疟疾耐药性是一个日益令人担忧的问题，影响到撒哈拉以南非洲和东南亚的许多地区。自2000年代末柬埔寨出现青蒿素耐药性以来，对其潜在机制的研究一直在进行中。2019年疟疾挑战提出了开发计算模型的任务，以解决推进防治疟疾斗争中的重要问题。第一个目标是准确预测恶性疟原虫分离株的青蒿素耐药性水平，IC50是量化的。第二个目标是根据体外转录图谱预测疟疾寄生虫分离株的寄生虫清除率。在这项工作中，我们使用新的方法开发机器学习模型，用于转换孤立的数据并处理这些数据转换练习产生的数万个变量。这是通过使用用于可伸缩机器学习的数据向量化的大规模并行处理来演示的。此外，我们还展示了集成机器学习模型在对这一挑战的两个目标进行高效预测方面的实用性。这是通过使用多种机器学习算法与各种缩放和归一化预处理步骤相结合来演示的。然后，使用投票集合，将多个模型组合以生成最终的模型预测。
Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC 50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.