Fang Yemin, Chen Lei
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Comb Chem High Throughput Screen. 2017;20(2):140-146. doi: 10.2174/1386207319666161215142130.
The study of metabolic pathway is one of the most important fields in biochemistry. Good comprehension of the metabolic pathway system is helpful to uncover the mechanism of some fundamental biological processes. Because chemicals are part of the main components of the metabolic pathway, correct identification of which metabolic pathways a given chemical can participate in is an important step for understanding the metabolic pathway system. Most previous methods only considered the chemical information, which tried to deal with a multilabel classification problem of assigning chemicals to proper metabolic pathways.
In this study, the pathway information was also employed, thereby transforming the problem into a binary classification problem of identifying the pair of chemicals and metabolic pathways, i.e., a chemical and a metabolic pathway was paired as a sample to be considered in this study. To construct the prediction model, the association between chemical pathway type pairs was evaluated by integrating the association between chemicals and association between pathway types. The support vector machine was adopted as the prediction engine.
The extensive tests show that the constructed model yields good performance with total prediction accuracy around 0.878.
The comparison results indicate that our model is quite effective and suitable for the identification of whether a given chemical can participate in a given metabolic pathway.
代谢途径的研究是生物化学中最重要的领域之一。对代谢途径系统的良好理解有助于揭示一些基本生物过程的机制。由于化学物质是代谢途径主要成分的一部分，正确识别给定化学物质可参与哪些代谢途径是理解代谢途径系统的重要一步。大多数先前的方法仅考虑化学信息，试图处理将化学物质分配到适当代谢途径的多标签分类问题。
在本研究中，还采用了途径信息，从而将问题转化为识别化学物质与代谢途径对的二元分类问题，即，将一种化学物质和一条代谢途径配对作为本研究中要考虑的一个样本。为构建预测模型，通过整合化学物质之间的关联和途径类型之间的关联来评估化学途径类型对之间的关联。采用支持向量机作为预测引擎。
广泛的测试表明，构建的模型具有良好的性能，总预测准确率约为0.878。
比较结果表明，我们的模型非常有效，适用于识别给定化学物质是否可参与给定的代谢途径。
