dc.contributor.author | Benstead-Hume, G | |
dc.contributor.author | Chen, X | |
dc.contributor.author | Hopkins, SR | |
dc.contributor.author | Lane, KA | |
dc.contributor.author | Downs, JA | |
dc.contributor.author | Pearl, FMG | |
dc.date.accessioned | 2019-09-23T13:41:42Z | |
dc.date.issued | 2019-04-17 | |
dc.identifier.citation | PLoS computational biology, 2019, 15 (4), pp. e1006888 - ? | |
dc.identifier.issn | 1553-734X | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/3362 | |
dc.identifier.eissn | 1553-7358 | |
dc.identifier.doi | 10.1371/journal.pcbi.1006888 | |
dc.description.abstract | In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development. | |
dc.format | Electronic-eCollection | |
dc.format.extent | e1006888 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | PUBLIC LIBRARY SCIENCE | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Animals | |
dc.subject | Humans | |
dc.subject | Neoplasms | |
dc.subject | Tumor Suppressor Proteins | |
dc.subject | Protein Interaction Mapping | |
dc.subject | Computational Biology | |
dc.subject | Genes, Essential | |
dc.subject | Multigene Family | |
dc.subject | Algorithms | |
dc.subject | Models, Biological | |
dc.subject | Artificial Intelligence | |
dc.subject | Drug Discovery | |
dc.subject | Synthetic Biology | |
dc.subject | Molecular Targeted Therapy | |
dc.subject | Protein Interaction Maps | |
dc.subject | Gene Ontology | |
dc.subject | Synthetic Lethal Mutations | |
dc.title | Predicting synthetic lethal interactions using conserved patterns in protein interaction networks. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2019-02-18 | |
rioxxterms.versionofrecord | 10.1371/journal.pcbi.1006888 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2019-04-17 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | PLoS computational biology | |
pubs.issue | 4 | |
pubs.notes | No embargo | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Biology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Biology/Epigenetics and Genome Stability | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Biology | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Biology/Epigenetics and Genome Stability | |
pubs.publication-status | Published | |
pubs.volume | 15 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Epigenetics and Genome Stability | |
dc.contributor.icrauthor | Downs, Jessica | |