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dc.contributor.authorAlthubaiti, Sara
dc.contributor.authorKulmanov, Maxat
dc.contributor.authorLiu, Yang
dc.contributor.authorGkoutos, Georgios
dc.contributor.authorSchofield, Paul N.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2021-03-31T11:15:09Z
dc.date.available2021-03-31T11:15:09Z
dc.date.issued2021-03-03
dc.identifier.citationAlthubaiti, S., Kulmanov, M., Liu, Y., Gkoutos, G., Schofield, P., & Hoehndorf, R. (2021). DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration. doi:10.1101/2021.03.02.433454
dc.identifier.doi10.1101/2021.03.02.433454
dc.identifier.urihttp://hdl.handle.net/10754/668443
dc.description.abstractCombining multiple types of genomic, transcriptional, proteomic, and epigenetic datasets has the potential to reveal biological mechanisms across multiple scales, and may lead to more accurate models for clinical decision support. Developing efficient models that can derive clinical outcomes from high-dimensional data remains problematical; challenges include the integration of multiple types of omics data, inclusion of biological background knowledge, and developing machine learning models that are able to deal with this high dimensionality while having only few samples from which to derive a model. We developed DeepMOCCA, a framework for multi-omics cancer analysis. We combine different types of omics data using biological relations between genes, transcripts, and proteins, combine the multi-omics data with background knowledge in the form of protein-protein interaction networks, and use graph convolution neural networks to exploit this combination of multi-omics data and background knowledge. DeepMOCCA predicts survival time for individual patient samples for 33 cancer types and outperforms most existing survival prediction methods. Moreover, DeepMOCCA includes a graph attention mechanism which prioritizes driver genes and prognostic markers in a patient-specific manner; the attention mechanism can be used to identify drivers and prognostic markers within cohorts and individual patients.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3790-01-01 and URF/1/4355-01-01. GVG acknowledges support from the NIHR Birmingham ECMC, the NIHR Birmingham SRMRC, the NIHR Birmingham Biomedical Research Centre, Nanocommons H2020-EU (731032), OpenRisknet.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttp://biorxiv.org/lookup/doi/10.1101/2021.03.02.433454
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration
dc.typePreprint
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputational Bioscience Research Center, Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionCollege of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, United Kingdom, and Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, B15 2TT, Birmingham, United Kingdom, and NIHR Biomedical Research Centre, B15 2TT, Birmingham, United Kingdom, and NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, United Kingdom, and MRC Health Data Research UK (HDR UK) Midlands, B15 2TT, Birmingham, United Kingdom.
dc.contributor.institutionDepartment of Physiology, Development, and Neuroscience, University of Cambridge, Downing Street, CB23EG, Cambridge, UK.
kaust.personAlthubaiti, Sara
kaust.personKulmanov, Maxat
kaust.personLiu, Yang
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3790-01-01
kaust.grant.numberURF/1/4355-01-01
refterms.dateFOA2021-03-31T11:17:59Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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