wilhelm-lab/dlomix
Python framework for Deep Learning in Proteomics
DLOmix helps proteomics researchers use deep learning to predict various properties of peptides from their sequences. You input peptide sequences and other relevant features, and the system outputs predictions for things like retention time, fragment ion intensities, or detectability in a mass spectrometer. This tool is designed for scientists working with proteomic data who want to apply deep learning models without building them from scratch.
Available on PyPI.
Use this if you are a proteomics researcher looking to apply deep learning to predict peptide characteristics like retention time or ion intensity, and you appreciate having pre-built models and a unified interface for popular deep learning frameworks.
Not ideal if you need a general-purpose deep learning framework for tasks outside of proteomics or if you prefer to build all deep learning models and data processing pipelines entirely from scratch.
Stars
39
Forks
13
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 02, 2026
Commits (30d)
0
Dependencies
9
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/wilhelm-lab/dlomix"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
CompOmics/DeepLC
DeepLC: Retention time prediction for peptides carrying any modification.
BojarLab/glycowork
Package for processing and analyzing glycans and their role in biology.
BojarLab/CandyCrunch
Predicting glycan structure from LC-MS/MS data
sidhomj/DeepTCR
Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data
MannLabs/alphapeptdeep
Deep learning framework for proteomics