BIMSBbioinfo/janggu
Deep learning infrastructure for genomics
This tool helps genomics researchers and computational biologists quickly build and test deep learning models to understand biological hypotheses. It takes standard genomic data formats like FASTA, BAM, BIGWIG, BED, and GFF files, processes them, and outputs predictions that can be visualized as genomic coverage tracks (BIGWIG files). It's designed for those who want to focus on designing neural network architectures for genomic data without getting bogged down in data preparation and evaluation.
257 stars and 83 monthly downloads. No commits in the last 6 months. Available on PyPI.
Use this if you are a genomics researcher looking to apply deep learning to genomic data, from acquisition to model evaluation, to test biological hypotheses efficiently.
Not ideal if you are solely working with non-genomic datasets or prefer to manage all data preprocessing and model evaluation steps manually.
Stars
257
Forks
35
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Sep 29, 2021
Monthly downloads
83
Commits (30d)
0
Dependencies
13
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/BIMSBbioinfo/janggu"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
aertslab/CREsted
CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data,...
AI-sandbox/gnomix
A fast, scalable, and accurate local ancestry method.
kr-colab/ReLERNN
Recombination Landscape Estimation using Recurrent Neural Networks
grimmlab/easyPheno
easyPheno: a model agnostic phenotype prediction framework
arnor-sigurdsson/EIR
A toolkit for training deep learning models on genotype, tabular, sequence, image, array and binary data.