Mining integrated semantic networks for drug repositioning opportunities
Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2016-01-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/1558.pdf |
| _version_ | 1797417749991915520 |
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| author | Joseph Mullen Simon J. Cockell Hannah Tipney Peter M. Woollard Anil Wipat |
| author_facet | Joseph Mullen Simon J. Cockell Hannah Tipney Peter M. Woollard Anil Wipat |
| author_sort | Joseph Mullen |
| collection | DOAJ |
| description | Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions. |
| first_indexed | 2024-03-09T06:24:13Z |
| format | Article |
| id | doaj.art-c900bfeded3049a7b0cb044f4d5386b4 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:24:13Z |
| publishDate | 2016-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-c900bfeded3049a7b0cb044f4d5386b42023-12-03T11:30:11ZengPeerJ Inc.PeerJ2167-83592016-01-014e155810.7717/peerj.1558Mining integrated semantic networks for drug repositioning opportunitiesJoseph Mullen0Simon J. Cockell1Hannah Tipney2Peter M. Woollard3Anil Wipat4Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science, University of Newcastle-upon-Tyne, Newcastle upon Tyne, United KingdomBioinformatics Support Unit, University of Newcastle-upon-Tyne, United KingdomComputational Biology, Target Sciences, GSK R&D, GlaxoSmithKline, Stevenage, Hertfordshire, United KingdomComputational Biology, Target Sciences, GSK R&D, GlaxoSmithKline, Stevenage, Hertfordshire, United KingdomInterdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science, University of Newcastle-upon-Tyne, Newcastle upon Tyne, United KingdomCurrent research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.https://peerj.com/articles/1558.pdfData integrationDrug repositioningSystems approachesData miningSemantic networksSemantic subgraphs |
| spellingShingle | Joseph Mullen Simon J. Cockell Hannah Tipney Peter M. Woollard Anil Wipat Mining integrated semantic networks for drug repositioning opportunities PeerJ Data integration Drug repositioning Systems approaches Data mining Semantic networks Semantic subgraphs |
| title | Mining integrated semantic networks for drug repositioning opportunities |
| title_full | Mining integrated semantic networks for drug repositioning opportunities |
| title_fullStr | Mining integrated semantic networks for drug repositioning opportunities |
| title_full_unstemmed | Mining integrated semantic networks for drug repositioning opportunities |
| title_short | Mining integrated semantic networks for drug repositioning opportunities |
| title_sort | mining integrated semantic networks for drug repositioning opportunities |
| topic | Data integration Drug repositioning Systems approaches Data mining Semantic networks Semantic subgraphs |
| url | https://peerj.com/articles/1558.pdf |
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