RasmussenLab
The Rasmussen group focuses on proteome and genome variation, coding variation and deep learning for integration of genomics, proteomics and clinical data.
Copenhagen
Pinned Repositories
CLASTER
Modeling nascent RNA transcription from chromatin landscape and structure
IntroToML
Machine Learning course at Copenhagen University, Faculty of Health and Medical Sciences, Center for Health Data Science
MOVE
MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
njab
Not just another biomarkers: Functionality for biomarker discovery
phamb
Downstream processing of VAMB binning for Viral Elucidation
pimms
Imputing proteomics data using deep learning models
python_package
Example Python package
taxconverter
Unifies the presentation of taxonomic classifiers output
vamb
Variational autoencoder for metagenomic binning
vCentenarian
Repository with supporting code and information for virome analysis of Centenarian metagenomes
RasmussenLab's Repositories
RasmussenLab/proteomics-metadata-standard
The Proteomics Experimental Design file format: Standard for experimental design annotation
RasmussenLab/StreptococcusMitis
In house python script used for Højholt et al. 2019