[1]聂长森[],白勇[],柳贤德[]. 基于机器学习的COX抑制剂预测模型研究[J].计算机技术与发展,2017,27(10):74-77.
　NIE Chang-sen[],BAI Yong[],LIU Xian-de[]. Study on COX Inhibitor Prediction Model Based on Machine Learning[J].,2017,27(10):74-77.
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 基于机器学习的COX抑制剂预测模型研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年10期
页码:
74-77
栏目:
智能、算法、系统工程
出版日期:
2017-10-10

文章信息/Info

Title:
 Study on COX Inhibitor Prediction Model Based on Machine Learning
文章编号:
1673-629X（2017)10-0001-04
作者:
 聂长森[1]; 白勇[1]; 柳贤德[2]
 1.海南大学 信息科学技术学院；2.海南大学 农学院
Author(s):
 NIE Chang-sen[1]; BAI Yong[1]; LIU Xian-de[2]
关键词:
 ; COX抑制剂; 机器学习方法; 自组织特征神经网络;  随机森林; 支持向量机
Keywords:
 ; COX inhibitors; machine learning; SOM; random forest; support vector machines
分类号:
TP301
文献标志码:
A
摘要:
 针对目前COX(环氧合酶)抑制剂较少且抑制效果差的问题,以及传统的化学实验筛选COX抑制剂分子的方法中成本高且效率低的问题,基于机器学习算法,提出并建立了一种COX抑制剂的预测模型.该模型可高效且准确地找到COX抑制剂,通过大量搜集文献中的数据建立数据集,使用Mold2软件计算化合物分子描述符,利用自组织特征映射神经网络(SOM)划分训练集和测试集,应用随机森林(RF)和支持向量机(SVM)等机器学习算法分别建立了COX抑制剂预测模型.实验对比发现,SOM结合RF算法较传统化学实验方法具有更好的预测精度,且预测效率也有大幅提升.实验研究表明,基于自组织神经网络和随机森立的机器学习方法建立的COX抑制剂预测模型,具有很好的分类预测效果,可以为COX抑制剂的分析与预测提供有力的研究工具.
Abstract:
 In allusion of the lack in COX ( Cyclooxygenase) inhibitor and its poor inhibition effect,moreover for the reason that the tradi-tional COX inhibitor screening must be performed through chemical experiment in high cost and low efficiency,a forecast model of COX inhibitors based on machine learning algorithm is proposed and established. It can find COX inhibitor efficiently and accurately. In the es-tablishing process the data set with huge collection of data in the literature has been built up and then the molecular descriptors with the software of Mold2 has been calculated and divided into training set and testing set with the method of SOM. However,two ML methods, Support Vector Machine (SVM) and Random Forest (RF),are employed to develop a prediction method for searching inhibitors and non-inhibitors of COX from the literature. The verification experiments show that the algorithm of SOM and RF has a better prediction accuracy,which also has a higher efficiency compared with the traditional chemical methods. The results of investigation demonstrate that the COX inhibitor prediction models based on SOM and RF has a good classification prediction effect and provides powerful instrument for analysis and prediction of COX inhibitor.

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更新日期/Last Update: 2017-11-23