For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
6
views
14
references
 Top references
cited by
59
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      834
      similar
       All similar
      • Record: found
      • Abstract: not found
      • Article: not found

      Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations

      Author(s): , , ,
      Publication date (Print):
      Journal: Bioinformatics
      Publisher: Oxford University Press (OUP)

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          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.

          Abstract

          A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.

          Related collections

          Most cited references14

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

          Linked Data - The Story So Far

          Christian Bizer, Tom Heath, Tim Berners-Lee (2011)
          Article 0 comments Cited 334 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Universal Protein Resource (UniProt)

            emmanuel boutet, Claire O'Donovan (2008)
            The Universal Protein Resource (UniProt) provides a stable, comprehensive, freely accessible, central resource on protein sequences and functional annotation. The UniProt Consortium is a collaboration between the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt website, and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of four major components, each optimized for different uses: the UniProt Knowledgebase, the UniProt Reference Clusters, the UniProt Archive and the UniProt Metagenomic and Environmental Sequences database. UniProt is updated and distributed every three weeks, and can be accessed online for searches or download at http://www.uniprot.org.
            Article 0 comments Cited 333 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

              Yoshihiro Yamanishi, Michihiro Araki, Alex Gutteridge … (2008)
              Motivation: The identification of interactions between drugs and target proteins is a key area in genomic drug discovery. Therefore, there is a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently. Results: In this article, we characterize four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, and reveal significant correlations between drug structure similarity, target sequence similarity and the drug–target interaction network topology. We then develop new statistical methods to predict unknown drug–target interaction networks from chemical structure and genomic sequence information simultaneously on a large scale. The originality of the proposed method lies in the formalization of the drug–target interaction inference as a supervised learning problem for a bipartite graph, the lack of need for 3D structure information of the target proteins, and in the integration of chemical and genomic spaces into a unified space that we call ‘pharmacological space’. In the results, we demonstrate the usefulness of our proposed method for the prediction of the four classes of drug–target interaction networks. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery. Availability: Softwares are available upon request. Contact: Yoshihiro.Yamanishi@ensmp.fr Supplementary information: Datasets and all prediction results are available at http://web.kuicr.kyoto-u.ac.jp/supp/yoshi/drugtarget/.
              Article 0 comments Cited 327 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Title: Bioinformatics
                Publisher: Oxford University Press (OUP)
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1460-2059
                Publication date Created: August 01 2017
                Publication date (Print): August 01 2017
                Volume: 33
                Issue: 15
                Pages: 2337-2344
                Article
                DOI: 10.1093/bioinformatics/btx160
                PMC ID: 5860112
                PubMed ID: 28430977
                SO-VID: e5718d71-6d84-4559-8f2c-772f9ff60d81
                Copyright © © 2017
                History

                Data availability:

                Comments

                Comment on this article

                scite_

                Similar content834

                See all similar

                Cited by58

                See all cited by

                Most referenced authors1,040

                See all reference authors