Personalized microbial network inference via co-regularized spectral clustering

Autor: Imangaliyev, S., Keijser, B.J., Crielaard, W., Tsivtsivadze, E., Zheng, H., Hu, X., Berrar, D., Wang, Y., Dubitzky, W., Hao, J.K., Cho, K.H., Gilbert, D.
Přispěvatelé: Preventive Dentistry, Zheng, H., Hu, X., Berrar, D., Wang, Y., Dubitzky, W., Hao, J.K., Cho, K.H., Gilbert, D., Preventieve tandheelkunde (OII, ACTA)
Jazyk: angličtina
Rok vydání: 2015
Předmět:
Zdroj: Proceedings: 2014 IEEE International Conference on Bioinformatics and Biomedicine: 2-5 November 2014, Belfast, UK, 484-488
STARTPAGE=484;ENDPAGE=488;TITLE=Proceedings: 2014 IEEE International Conference on Bioinformatics and Biomedicine: 2-5 November 2014, Belfast, UK
Zheng, H.Hu, X.T.Wang, Y.Hao, J.K.Gilbert, D.Berrar, D.Cho, K.H.Dubitzky, W., Proceedings-2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014, 484-488
BIBM
Imangaliyev, S, Keijser, B J, Crielaard, W & Tsivtsivadze, E 2014, Personalized microbial network inference via co-regularized spectral clustering . in H Zheng, X Hu, D Berrar, Y Wang, W Dubitzky, J K Hao, K H Cho & D Gilbert (eds), Proceedings: 2014 IEEE International Conference on Bioinformatics and Biomedicine: 2-5 November 2014, Belfast, UK . IEEE, Danvers, MA, pp. 484-488, 2014 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2014), 2/11/14 . https://doi.org/10.1109/BIBM.2014.6999205
Imangaliyev, S, Keijser, B, Crielaard, W & Tsivtsivadze, E 2015, ' Personalized microbial network inference via co-regularized spectral clustering ', Methods, vol. 83, pp. 28-35 . https://doi.org/10.1016/j.ymeth.2015.03.017
Methods, 83, 28-35. Academic Press Inc.
ISSN: 1046-2023
DOI: 10.1016/j.ymeth.2015.03.017
Popis: We use Human Microbiome Project (HMP) cohort (Peterson et al., 2009) 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. The scripts written in scientific Python and in Matlab, which were used for network visualization, are provided for download on the website http://learning-machines.com/.
Databáze: OpenAIRE