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Understanding microbiome dynamics via interpretable graph representation learning. (arXiv:2203.01830v2 [q-bio.QM] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Large-scale perturbations in the microbiome constitution are strongly
correlated, whether as a driver or a consequence, with the health and
functioning of human physiology. However, understanding the difference in the
microbiome profiles of healthy and ill individuals can be complicated due to
the large number of complex interactions among microbes. We propose to model
these interactions as a time-evolving graph whose nodes are microbes and edges
are interactions among them. Motivated by the need to analyse such complex
interactions, we …
arxiv bio dynamics graph graph representation microbiome representation representation learning understanding