Personalized microbial network inference via co-regularized spectral clustering

S. Imangaliyev, B.J. Keijser, W. Crielaard, E. Tsivtsivadze

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademic

Abstract

We use Human Microbiome Project (HMP) cohort [1] to infer personalized oral microbial networks of healthy individuals. To determine clustering of individuals with similar microbial profiles, co-regularized spectral clustering algorithm is applied to the dataset. For each cluster we discovered, we compute co-occurrence relationships among the microbial species that determine microbial network per cluster of individuals. The results of our study suggest that there are several differences in microbial interactions on personalized network level in healthy oral samples acquired from various niches. Based on the results of co-regularized spectral clustering we discover two groups of individuals with different topology of their microbial interaction network. The results of microbial network inference suggest that niche-wise interactions are different in these two groups. Our study shows that healthy individuals have different microbial clusters according to their oral microbiota. Such personalized microbial networks open a better understanding of the microbial ecology of healthy oral cavities and new possibilities for future targeted medication.
Original languageEnglish
Title of host publicationProceedings: 2014 IEEE International Conference on Bioinformatics and Biomedicine: 2-5 November 2014, Belfast, UK
EditorsH. Zheng, X. Hu, D. Berrar, Y. Wang, W. Dubitzky, J.K. Hao, K.H. Cho, D. Gilbert
Place of PublicationDanvers, MA
PublisherIEEE
Pages484-488
ISBN (Print)9781479956692
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2014) -
Duration: 2 Nov 20145 Nov 2014

Conference

Conference2014 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2014)
Period2/11/145/11/14

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