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dc.contributor.authorLiang, Y.
dc.contributor.authorKelemen, A.
dc.date.accessioned2019-09-19T18:35:47Z
dc.date.available2019-09-19T18:35:47Z
dc.date.issued2017
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85023744571&doi=10.1186%2fs13040-017-0140-x&partnerID=40&md5=8ddfaec23188cbe1a8f5c4681286019e
dc.identifier.urihttp://hdl.handle.net/10713/10970
dc.description.abstractModeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail. Copyright The Author(s). 2017.en_US
dc.description.urihttps://doi.org/10.1186/s13040-017-0140-xen_US
dc.language.isoen_USen_US
dc.publisherBioMed Central Ltd.en_US
dc.relation.ispartofBioData Mining
dc.subjectCausal networken_US
dc.subjectComputational dynamic approaches for temporal omics data with applications to systems medicineen_US
dc.subjectDynamic approachesen_US
dc.subjectSystems medicineen_US
dc.subjectTemporal omics dataen_US
dc.subjectTrajectory predictionen_US
dc.titleComputational dynamic approaches for temporal omics data with applications to systems medicineen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s13040-017-0140-x


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