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dc.contributor.authorSimidjievski, Nikola
dc.contributor.authorBodnar, Cristian
dc.contributor.authorTariq, Ifrah
dc.contributor.authorScherer, Paul
dc.contributor.authorAndres Terre, Helena
dc.contributor.authorShams, Zohreh
dc.contributor.authorJamnik, Mateja
dc.contributor.authorLiò, Pietro
dc.date.accessioned2019-12-25T06:10:18Z
dc.date.available2019-12-25T06:10:18Z
dc.date.issued2019-12-11
dc.date.submitted2019-07-29
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/300254
dc.description.abstractInternational initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyze such data, several machine learning, bioinformatics, and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyze multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.
dc.languageen
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectGenetics
dc.subjectmachine learning
dc.subjectcancer–breast cancer
dc.subjectvariational autoencoder
dc.subjectdeep learning
dc.subjectintegrative data analyses
dc.subjectartificial intelligence
dc.subjectbioinformactics
dc.subjectmulti-omic analysis
dc.titleVariational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice
dc.typeArticle
dc.date.updated2019-12-25T06:10:17Z
dc.identifier.doi10.17863/CAM.47327
dcterms.dateAccepted2019-10-31
rioxxterms.versionofrecord10.3389/fgene.2019.01205
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn1664-8021


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)