Model-free prediction of microbiome compositions

Eitan E E. Asher, Amir Bashan

Research output: Contribution to journalArticlepeer-review


Background: The recent recognition of the importance of the microbiome to the host’s health and well-being has yielded efforts to develop therapies that aim to shift the microbiome from a disease-associated state to a healthier one. Direct manipulation techniques of the species’ assemblage are currently available, e.g., using probiotics or narrow-spectrum antibiotics to introduce or eliminate specific taxa. However, predicting the species’ abundances at the new state remains a challenge, mainly due to the difficulties of deciphering the delicate underlying network of ecological interactions or constructing a predictive model for such complex ecosystems. Results: Here, we propose a model-free method to predict the species’ abundances at the new steady state based on their presence/absence configuration by utilizing a multi-dimensional k-nearest-neighbors (kNN) regression algorithm. By analyzing data from numeric simulations of ecological dynamics, we show that our predictions, which consider the presence/absence of all species holistically, outperform both the null model that uses the statistics of each species independently and a predictive neural network model. We analyze real metagenomic data of human-associated microbial communities and find that by relying on a small number of “neighboring” samples, i.e., samples with similar species assemblage, the kNN predicts the species abundance better than the whole-cohort average. By studying both real metagenomic and simulated data, we show that the predictability of our method is tightly related to the dissimilarity-overlap relationship of the training data. Conclusions: Our results demonstrate how model-free methods can prove useful in predicting microbial communities and may facilitate the development of microbial-based therapies. [MediaObject not available: see fulltext.].

Original languageEnglish
Article number17
Issue number1
StatePublished - 1 Feb 2024

Bibliographical note

Publisher Copyright:
© 2024, The Author(s).


This research was supported by the Israel Science Foundation (grant No. 1258/21) and the German-Israeli Foundation for Scientific Research and Development (grant No. I-1523-500.15/2021).

FundersFunder number
German-Israeli Foundation for Scientific Research and DevelopmentI-1523-500.15/2021
Israel Science Foundation1258/21


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