Predicting drug-disease associations with heterogeneous network embedding.

The prediction of drug-disease associations holds great potential for precision medicine in the era of big data and is important for the identification of new indications for existing drugs. The associations between drugs and diseases can be regarded as a complex heterogeneous network with multiple types of nodes and links. In this paper, we propose a method, namely HED (Heterogeneous network Embedding for Drug-disease association), to predict potential associations between drugs and diseases based on a drug-disease heterogeneous network. Specifically, with the heterogeneous network constructed from known drug-disease associations, HED employs network embedding to characterize drug-disease associations and then trains a classifier to predict novel potential drug-disease associations. The results on two real datasets show that HED outperforms existing popular approaches. Furthermore, some of our predictions have been verified by evidence from literature. For instance, carvedilol, a drug that was originally used for heart failure, left ventricular dysfunction, and hypertension, is predicted to be useful for atrial fibrillation by HED, which is supported by clinical trials.

[1]  Juan Wang,et al.  The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method , 2018, BMC Bioinformatics.

[2]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[3]  Xiangxiang Zeng,et al.  Prediction of Drug–Gene Interaction by Using Metapath2vec , 2018, Front. Genet..

[4]  Anaïs Baudot,et al.  Random Walk With Restart on Multiplex and Heterogeneous Biological Networks , 2017, bioRxiv.

[5]  Yi Pan,et al.  Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm , 2016, Bioinform..

[6]  Jian Pei,et al.  A Survey on Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[7]  R. Sharan,et al.  PREDICT: a method for inferring novel drug indications with application to personalized medicine , 2011, Molecular systems biology.

[8]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[9]  Jian Peng,et al.  A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information , 2017, Nature Communications.

[10]  Johan A. K. Suykens,et al.  Optimal control by least squares support vector machines , 2001, Neural Networks.

[11]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2019 , 2018, Nucleic Acids Res..

[12]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[13]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[14]  John O. Woods,et al.  Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses , 2013, PloS one.

[15]  Jagdish Chandra Patra,et al.  Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network , 2010, Bioinform..

[16]  Natalia Novac,et al.  Challenges and opportunities of drug repositioning. , 2013, Trends in pharmacological sciences.

[17]  Zuping Zhang,et al.  Prediction of Drug-Disease Associations for Drug Repositioning Through Drug-miRNA-Disease Heterogeneous Network , 2018, IEEE Access.

[18]  T. Ashburn,et al.  Drug repositioning: identifying and developing new uses for existing drugs , 2004, Nature Reviews Drug Discovery.

[19]  Hojung Nam,et al.  Drug repositioning of herbal compounds via a machine-learning approach , 2019, BMC Bioinformatics.

[20]  Feng Liu,et al.  Predicting drug-disease associations by using similarity constrained matrix factorization , 2018, BMC Bioinformatics.

[21]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[22]  Hsiang-Yuan Yeh,et al.  Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation , 2013, BMC Medical Genomics.

[23]  Armando Blanco,et al.  DrugNet: Network-based drug-disease prioritization by integrating heterogeneous data , 2015, Artif. Intell. Medicine.

[24]  Hao Ye,et al.  Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning , 2014, PloS one.

[25]  Xiang Zhang,et al.  Drug repositioning by integrating target information through a heterogeneous network model , 2014, Bioinform..