An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.
Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes...
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155811&type=printable |
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| author | Joseph Mullen Simon J Cockell Peter Woollard Anil Wipat |
| author_facet | Joseph Mullen Simon J Cockell Peter Woollard Anil Wipat |
| author_sort | Joseph Mullen |
| collection | DOAJ |
| description | Drug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm. |
| format | Article |
| id | doaj-art-b4f4c01aea1b4b33be94739b4b8ec5bf |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b4f4c01aea1b4b33be94739b4b8ec5bf2025-08-20T03:11:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01115e015581110.1371/journal.pone.0155811An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations.Joseph MullenSimon J CockellPeter WoollardAnil WipatDrug development is both increasing in cost whilst decreasing in productivity. There is a general acceptance that the current paradigm of R&D needs to change. One alternative approach is drug repositioning. With target-based approaches utilised heavily in the field of drug discovery, it becomes increasingly necessary to have a systematic method to rank gene-disease associations. Although methods already exist to collect, integrate and score these associations, they are often not a reliable reflection of expert knowledge. Furthermore, the amount of data available in all areas covered by bioinformatics is increasing dramatically year on year. It thus makes sense to move away from more generalised hypothesis driven approaches to research to one that allows data to generate their own hypothesis. We introduce an integrated, data driven approach to drug repositioning. We first apply a Bayesian statistics approach to rank 309,885 gene-disease associations using existing knowledge. Ranked associations are then integrated with other biological data to produce a semantically-rich drug discovery network. Using this network, we show how our approach identifies diseases of the central nervous system (CNS) to be an area of interest. CNS disorders are identified due to the low numbers of such disorders that currently have marketed treatments, in comparison to other therapeutic areas. We then systematically mine our network for semantic subgraphs that allow us to infer drug-disease relations that are not captured in the network. We identify and rank 275,934 drug-disease has_indication associations after filtering those that are more likely to be side effects, whilst commenting on the top ranked associations in more detail. The dataset has been created in Neo4j and is available for download at https://bitbucket.org/ncl-intbio/genediseaserepositioning along with a Java implementation of the searching algorithm.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155811&type=printable |
| spellingShingle | Joseph Mullen Simon J Cockell Peter Woollard Anil Wipat An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations. PLoS ONE |
| title | An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations. |
| title_full | An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations. |
| title_fullStr | An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations. |
| title_full_unstemmed | An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations. |
| title_short | An Integrated Data Driven Approach to Drug Repositioning Using Gene-Disease Associations. |
| title_sort | integrated data driven approach to drug repositioning using gene disease associations |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155811&type=printable |
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