An Ensemble Deep-Learning Approach to Predicting the "Theater of Activity" in a Microbiome.

About DeepToA

DeepToA is an ensemble deep-Learning approach to predicting the "Theater of Activity" in a Microbiome. Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a "theater of activity" (ToA). To what degree does the taxonomic and functional content of the former depend on the (details of the) latter? More technically, given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA?
The joint work, is mostly promoted by Wenhuan Zeng (University of Tübingen), Anupam Gautam (University of Tübingen & IMPRS-"From Molecules to Organisms", Max Planck Institute for Biology Tübingen), and Daniel H. Huson (University of Tübingen). We encourage all researcher around the world to contact us and to share your ideas on how to improve or extend our services.

1) An Ensemble Deep-Learning Approach to Predicting the Theater of Activity of a Microbiome. Wenhuan Zeng, Anupam Gautam, Daniel H. Huson. (Oxford) Bioinformatics, August 2022 (Link).
2) Preprint can be found here (Link).


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Select Metagenomic Profile Table:
Select Functional Table:
Note: These are just small example datasets made for users,
by removing taxa and function for original tables, to show
how tables should be. If you are using example tables,
select a case will be:- MGnify taxa id and formatted taxonomy.
Download example Metagenomic profile table:W3Schools
Download example Functional Table:W3Schools

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DeepToA v.1.0.2 - July 2022
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