A unified frame of predicting side effects of drugs by using linear neighborhood similarity

BackgroundDrug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions.ResultsIn this paper, we present a novel measure of drug-drug similarity named “linear neighborhood similarity”, which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method “LNSM” and its extension “LNSM-SMI” to predict side effects of new drugs, and propose the method “LNSM-MSE” to predict unobserved side effect of approved drugs.ConclusionsWe evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

[1]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[2]  Runtao Yang,et al.  An Ensemble Method with Hybrid Features to Identify Extracellular Matrix Proteins , 2015, PloS one.

[3]  Yoshihiro Yamanishi,et al.  Drug Side-Effect Prediction Based on the Integration of Chemical and Biological Spaces , 2012, J. Chem. Inf. Model..

[4]  Abdollah Dehzangi,et al.  A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  S. Bryant,et al.  PubChem as a public resource for drug discovery. , 2010, Drug discovery today.

[6]  Philip E. Bourne,et al.  Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors , 2009, PLoS Comput. Biol..

[7]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[8]  Yoshihiro Yamanishi,et al.  Predicting drug side-effect profiles: a chemical fragment-based approach , 2011, BMC Bioinformatics.

[9]  R. Krauss,et al.  When good drugs go bad , 2007, Nature.

[10]  Feng Liu,et al.  Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data , 2017, BMC Bioinformatics.

[11]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[12]  P. Bork,et al.  A side effect resource to capture phenotypic effects of drugs , 2010, Molecular systems biology.

[13]  Hiroshi Mamitsuka,et al.  Ensemble approaches for improving HLA class I-peptide binding prediction. , 2011, Journal of immunological methods.

[14]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[15]  J. Chen,et al.  Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures , 2013, Proteomics.

[16]  Jie Shen,et al.  Adverse Drug Events: Database Construction and in Silico Prediction , 2013, J. Chem. Inf. Model..

[17]  A. Fliri,et al.  Analysis of drug-induced effect patterns to link structure and side effects of medicines , 2005, Nature chemical biology.

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

[19]  Yanli Wang,et al.  PubChem: a public information system for analyzing bioactivities of small molecules , 2009, Nucleic Acids Res..

[20]  Gang Tian,et al.  Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features , 2016, PloS one.

[21]  F. J. C. Varona,et al.  10. Merle L, Laroche ML, Dantoine T, Charmes JP. Predicting and preventing adverse drugs reactions in the very old. Drugs Aging. 2005;22:375-92. , 2016 .

[22]  R. Altman,et al.  Predicting drug side-effects by chemical systems biology , 2009, Genome Biology.

[23]  J. Chen,et al.  Predicting adverse side effects of drugs , 2011, BMC Genomics.

[24]  D. Bojanic,et al.  Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development. , 2005, Drug discovery today.

[25]  Hisashi Kashima,et al.  Side Effect Prediction Using Cooperative Pathways , 2009, 2009 IEEE International Conference on Bioinformatics and Biomedicine.

[26]  Hua Zou,et al.  Predicting potential side effects of drugs by recommender methods and ensemble learning , 2016, Neurocomputing.

[27]  Hua Xu,et al.  Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs , 2012, J. Am. Medical Informatics Assoc..

[28]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[29]  Bin Chen,et al.  Gaining Insight into Off-Target Mediated Effects of Drug Candidates with a Comprehensive Systems Chemical Biology Analysis , 2009, J. Chem. Inf. Model..

[30]  Yoshihiro Yamanishi,et al.  Relating drug–protein interaction network with drug side effects , 2012, Bioinform..

[31]  Yuichi Sugiyama,et al.  Impact of Drug Transporter Studies on Drug Discovery and Development , 2003, Pharmacological Reviews.

[32]  Fei Wang,et al.  Label Propagation through Linear Neighborhoods , 2006, IEEE Transactions on Knowledge and Data Engineering.

[33]  Feng Liu,et al.  Drug side effect prediction through linear neighborhoods and multiple data source integration , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[34]  T. Dantoine,et al.  Predicting and Preventing Adverse Drug Reactions in the Very Old , 2005, Drugs & aging.

[35]  Juan Liu,et al.  Computational Prediction of Conformational B-Cell Epitopes from Antigen Primary Structures by Ensemble Learning , 2012, PloS one.

[36]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[37]  Feng Liu,et al.  A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs , 2016, BMC Bioinformatics.

[38]  Feng Liu,et al.  Predicting drug side effects by multi-label learning and ensemble learning , 2015, BMC Bioinformatics.

[39]  A. Bender,et al.  Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure , 2007, ChemMedChem.