GraphKKE: graph Kernel Koopman embedding for human microbiome analysis

dc.contributor.authorMelnyk, Kateryna
dc.contributor.authorKlus, Stefan
dc.contributor.authorMontavon, Grégoire
dc.contributor.authorConrad, Tim O. F.
dc.date.accessioned2023-08-02T11:58:31Z
dc.date.available2023-08-02T11:58:31Z
dc.date.issued2020-12-01
dc.date.updated2023-06-15T12:42:55Z
dc.description.abstractMore and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes.en
dc.description.sponsorshipDFG, 390685689, EXC 2046: MATH+: Berlin Mathematics Research Center
dc.description.sponsorshipBMBF, 01IS14013A, BBDC - Berliner Kompetenzzentrum für Big Data
dc.description.sponsorshipBMBF, 01IS18037J, BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Data
dc.identifier.eissn2364-8228
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/19210
dc.identifier.urihttps://doi.org/10.14279/depositonce-18006
dc.language.isoen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik
dc.subject.othertime-evolving graphsen
dc.subject.othergraph embeddingen
dc.subject.othergraph analysisen
dc.subject.othermachine learningen
dc.subject.otherbiological networksen
dc.subject.othermicrobiologyen
dc.titleGraphKKE: graph Kernel Koopman embedding for human microbiome analysisen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber96
dcterms.bibliographicCitation.doi10.1007/s41109-020-00339-2
dcterms.bibliographicCitation.issue1
dcterms.bibliographicCitation.journaltitleApplied Network Science
dcterms.bibliographicCitation.originalpublishernameSpringer
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.volume5
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen
tub.publisher.universityorinstitutionTechnische Universität Berlin

Files

Original bundle
Now showing 1 - 1 of 1
Loading…
Thumbnail Image
Name:
s41109-020-00339-2.pdf
Size:
3.12 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.86 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections