+1 Recommend
1 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Drug repositioning using drug-disease vectors based on an integrated network

      BMC Bioinformatics
      BioMed Central
      Network biology, Drug repositioning, Gene regulation, Protein interaction

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.



          Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network.


          We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease.


          We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).

          Electronic supplementary material

          The online version of this article (10.1186/s12859-018-2490-x) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: not found
          • Article: not found

          Building Predictive Models inRUsing thecaretPackage

            • Record: found
            • Abstract: found
            • Article: not found

            Random forest: a classification and regression tool for compound classification and QSAR modeling.

            A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
              • Record: found
              • Abstract: not found
              • Article: not found

              KEGG: Kyoto Encyclopedia of Genes and Genomes


                Author and article information

                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                21 November 2018
                21 November 2018
                : 19
                : 446
                ISNI 0000 0004 0647 2973, GRID grid.256155.0, Department of Computer Engineering, , Gachon University, 5-22Ho, IT college, ; 1324 Seongnam-daero, Seongnam-si, 13120 South Korea
                Author information
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                : 25 June 2018
                : 12 November 2018
                Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;
                Award ID: NRF-2018R1A2B6006223
                Award Recipient :
                Research Article
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                network biology,drug repositioning,gene regulation,protein interaction


                Comment on this article