microsoft/BindVAE
Variational Auto Encoders for learning binding signatures of transcription factors
This tool helps computational biologists and geneticists analyze DNA sequences to understand how transcription factors bind. You provide DNA sequence data (specifically k-mer features) from experiments like ChIP-seq or ATAC-seq, and it identifies distinct 'binding signatures' or patterns within those sequences. The output reveals latent patterns of k-mer enrichment and topic distributions, shedding light on the underlying regulatory mechanisms.
No commits in the last 6 months.
Use this if you need to uncover hidden, interpretable binding patterns of transcription factors from high-throughput sequencing data like ChIP-seq or ATAC-seq.
Not ideal if you are looking for a simple motif discovery tool or are not comfortable with command-line interfaces and managing machine learning dependencies.
Stars
14
Forks
5
Language
Python
License
MIT
Last pushed
Mar 14, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/microsoft/BindVAE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Naresh1318/Adversarial_Autoencoder
A wizard's guide to Adversarial Autoencoders
mseitzer/pytorch-fid
Compute FID scores with PyTorch.
acids-ircam/RAVE
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
ratschlab/aestetik
AESTETIK: Convolutional autoencoder for learning spot representations from spatial...
jaanli/variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)