首页> 外文会议>IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology >Application of Machine Learning Approaches on Quantitative Structure Activity Relationships
【24h】

Application of Machine Learning Approaches on Quantitative Structure Activity Relationships

机译:机器学习方法在定量结构活动关系中的应用

获取原文

摘要

Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as active or inactive with respect to a specific target biological system. This paper presents a comparison of Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Decision Trees (DT) in an effort to identify potentiators of metabotropic glutamate receptor 5 (mGluR5), compounds that have potential as novel treatments against schizophrenia. When training and testing each of the three techniques on the same dataset enrichments of 61, 64, and 43 were obtained and an area under the curve (AUC) of 0.77, 0.78, and 0.63 was determined for ANNs, SVMs, and DTs, respectively. For the top percentile of predicted active compounds, the true positives for all three methods were highly similar, while the inactives were diverse offering the potential use of jury approaches to improve prediction accuracy.
机译:成功应用机器学习技术以建立化学结构和生物活性(QSAR)之间的定量关系,即对特定目标生物系统的活性或无活性的分类化合物。本文呈现了人工神经网络(ANN),支持向量机(SVM)和决策树(DT)的比较,以识别代谢谷氨酸受体5(MGLUR5),具有潜在对精神分裂症的新治疗的化合物的增强剂。当获得61,64和43的相同数据集富集中的三种技术中的每一个时,分别为ANNS,SVM和DTS测定0.77,0.78和0.63的曲线(AUC)下的面积。对于预测的活性化合物的顶部百分点,所有三种方法的真正阳性非常相似,而否则是多样化的,提供陪审团方法的潜在使用来提高预测准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号