Liang Xujun, Zhang Pengfei, Yan Lu, Fu Ying, Peng Fang, Qu Lingzhi, Shao Meiying, Chen Yongheng, Chen Zhuchu
Bioinformatics. 2017 Apr 15;33(8):1187-1196. doi: 10.1093/bioinformatics/btw770.
: Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem.
: In this research, we present a novel method for indication prediction of both new drugs and approved drugs. This method is based on Laplacian regularized sparse subspace learning (LRSSL), which integrates drug chemical information, drug target domain information and target annotation information. Experimental results show that the proposed method outperforms several recent approaches for predicting drug-disease associations. Some drug therapeutic effects predicted by the method could be validated by database records or literatures. Moreover, with L1-norm constraint, important drug features have been extracted from multiple drug feature profiles. Case studies suggest that the extracted drug features could be beneficial to interpretation of the predicted results.
https://github.com/LiangXujun/LRSSL.
Supplementary data are available at Bioinformatics online.
探索药物的潜在疗效对于有效的药物开发至关重要。先前的研究表明，整合多种类型的信息有助于发现药物的新适应症。然而，在整合来自不同来源的数据时，如何有效地识别药物与疾病关联背后的机制仍然是一个具有挑战性的问题。
在本研究中，我们提出了一种用于预测新药和已批准药物适应症的新方法。该方法基于拉普拉斯正则化稀疏子空间学习（LRSSL），它整合了药物化学信息、药物靶标领域信息和靶标注释信息。实验结果表明，所提出的方法在预测药物与疾病关联方面优于最近的几种方法。该方法预测的一些药物治疗效果可以通过数据库记录或文献得到验证。此外，通过L1范数约束，从多个药物特征概况中提取了重要的药物特征。案例研究表明，提取的药物特征有助于解释预测结果。
https://github.com/LiangXujun/LRSSL。
补充数据可在《生物信息学》在线获取。
