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MICROBIAL NETWORK

Enhancing biomarkers with co-abundance

Nature Computational Science volume 2, pages 290–291 (2022)Cite this article

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The identification of robust and generalizable biomarkers based on microbial abundance data is a challenging task. An algorithm shows an enhanced classification performance by quantifying shifts in microbial co-abundances.

Microbial abundances can now be routinely quantified with sequencing-based profiling techniques but the abundance-based biomarkers have shown only limited accuracy in disease classification tasks. Moreover, the identified biomarker species often differ between studies. Practitioners of microbiome bioinformatics are increasingly exploiting sub-communities and network motifs as a unit of analysis, and finding ways to aggregate evidence across multiple studies1. However, the biomarker identification for disease classification is typically based on microbial abundance data, but seldom utilizes network-based information in taxonomic profiling studies. Writing in this issue of Nature Computational Science, Xiao et al2. introduce a network-based approach, NetMoss, which applies an idea that abundance-based analyses can be complemented by quantifying changes in the structure of the co-abundance networks to obtain enhanced biomarkers of health and disease.

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Fig. 1: Determining biomarkers with the shifts in the microbial network around a given (blue) species.

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  1. Department of Computing, University of Turku, Turku, Finland

    Leo Lahti

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  1. Leo Lahti
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Correspondence to Leo Lahti.

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The author declares no competing interests.

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Lahti, L. Enhancing biomarkers with co-abundance. Nat Comput Sci 2, 290–291 (2022). https://doi.org/10.1038/s43588-022-00254-9

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  • DOI: https://doi.org/10.1038/s43588-022-00254-9

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