Hailin Chen1, Zuping Zhang2, Jingpu Zhang3. 1. School of Software, East China Jiaotong University, Nanchang, 330013, China. chl_mail@csu.edu.cn. 2. School of Computer Science and Engineering, Central South University, Changsha, 410083, China. 3. School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, 467000, China.
Abstract
BACKGROUND: Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning. RESULTS: In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values. CONCLUSIONS: Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.
BACKGROUND: Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning. RESULTS: In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values. CONCLUSIONS: Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.
Entities:
Keywords:
Drug feature; Drug repositioning; Similarity fusion
Authors: Chien-Hung Huang; Jin-Shuei Ciou; Shun-Tsung Chen; Victor C Kok; Yi Chung; Jeffrey J P Tsai; Nilubon Kurubanjerdjit; Chi-Ying F Huang; Ka-Lok Ng Journal: PeerJ Date: 2016-09-28 Impact factor: 2.984
Authors: David S Wishart; Yannick D Feunang; An C Guo; Elvis J Lo; Ana Marcu; Jason R Grant; Tanvir Sajed; Daniel Johnson; Carin Li; Zinat Sayeeda; Nazanin Assempour; Ithayavani Iynkkaran; Yifeng Liu; Adam Maciejewski; Nicola Gale; Alex Wilson; Lucy Chin; Ryan Cummings; Diana Le; Allison Pon; Craig Knox; Michael Wilson Journal: Nucleic Acids Res Date: 2018-01-04 Impact factor: 16.971