Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data

We use open source human gut microbiome data to learn a microbial “language” model by adapting techniques from Natural Language Processing (NLP). Our microbial “language” model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial species and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single bacteria species to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different bacteria species in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it p..., No additional raw data was collected for this project. All inputs are available publicly. American Gut Project, Halfvarson, and Schirmer raw data are available from the NCBI database (accession numbers PRJEB11419, PRJEB18471, and PRJNA398089, respectively). We used the curated data produced by Tataru and David, 2020., , # Code and data for \"Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data\"

## Data:

* vocab_embeddings.npy
* Fixed vocabulary embeddings produced from prior work: [Decoding the language of microbiomes using word-embedding techniques, and applications in inflammatory bowel disease](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007859). Adapted from [here](http://files.cqls.oregonstate.edu/David_Lab/microbiome_embeddings/data/embed/).
* microbiomedata.zip
* Contains the labels and data for the three datasets used in this study. Specifically, it includes:
* IBD_(test|train)*(512|otu).npy and IBD*(test|train)_labels.npy
* halfvarson_(512_otu|otu).npy and halfvarson_IBD_labels.npy
* schirmer_IBD_(512_otu|otu).npy and schirmer_IBD_labels.npy
* (test|train)encodings_(512|1897).npy
* The data are stored as n_samples x max_sample_size x 2 numpy arrays, containin...