Drug clearance pathway prediction based on semi-supervised learning
Drug clearance pathway prediction based on semi-supervised learning
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DOI：
10.2197/ipsjtbio.8.21
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发表时间：
2015-03
影响因子：
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通讯作者：
Keisuke Yanagisawa;T. Ishida;Y. Akiyama
中科院分区：
文献类型：
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作者：
Keisuke Yanagisawa;T. Ishida;Y. Akiyama
有必要确认一种新药可以适当地从人体内清除。然而，检查药物在人体内的清除途径需要临床试验，因此需要很大的成本。因此，研究了药物清除途径预测的计算方法。先前提出的预测方法基于监督学习算法，该算法需要训练集中所有药物的清除途径信息作为输入标签。然而，由于获取这些数据的成本很高，这些数据往往是不够的。在本文中，我们提出了一种基于半监督学习的药物清除途径预测方法，该方法既可以使用标记数据，也可以使用未标记数据。我们评估了我们的方法的有效性，重点关注细胞色素P450 2C19酶，它参与了一个主要的清除途径。
It is necessary to confirm that a new drug can be appropriately cleared from the human body. However, checking the clearance pathway of a drug in the human body requires clinical trials, and therefore requires large cost. Thus, computational methods for drug clearance pathway prediction have been studied. The proposed prediction methods developed previously were based on a supervised learning algorithm, which requires clearance pathway information for all drugs in a training set as input labels. However, these data are often insufficient because of the high cost of their acquisition. In this paper, we propose a new drug clearance pathway prediction method based on semisupervised learning, which can use not only labeled data but also unlabeled data. We evaluated the effectiveness of our method, focusing on the cytochrome P450 2C19 enzyme, which is involved in one of the major clearance pathways.