This repository is for the preprint "A generative nonparametric Bayesian model for whole genomes"

Related tags

Deep Learningtensorflowprobabilistic-programmingbiological-data-analysisgenomes
Overview

BEAR

Overview

This repository contains code associated with the preprint A generative nonparametric Bayesian model for whole genomes (2021), which proposes Bayesian embedded autoregresive (BEAR) models. The repository provides example BEAR models as well as tools for implementing new models. It enables building, training and evaluating BEAR models on large scale sequencing datasets, including whole genome, transcriptomic and metagenomic data.

Documentation

For instructions on running examples and deploying the BEAR model, consult the documentation at https://bear-model.readthedocs.io/en/latest/.

Authors

This is a project of the Marks Lab in the Systems Biology Department at Harvard Medical School. It was developed by

License

This project is available under the MIT license.

Reference

Preprint: A. N. Amin*, E. N. Weinstein*, D. S. Marks, A generative nonparametric Bayesian model for whole genomes, 2021 (* equal contribution). https://www.biorxiv.org/content/10.1101/2021.05.30.446360v1

Owner
Debora Marks Lab
Debora Marks Lab
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