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
EIR
A toolkit for training deep learning models on genotype, tabular, sequence, image, array and binary data.
hela_qc_mnt_data
Functionality for processing and analysing HeLa data quality control (QC) and maintenance (MNT) samples processed by MaxQuant
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
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/vamb
Variational autoencoder for metagenomic binning
RasmussenLab/MOVE
MOVE (Multi-Omics Variational autoEncoder) for integrating multi-omics data and identifying cross modal associations
RasmussenLab/phamb
Downstream processing of VAMB binning for Viral Elucidation
RasmussenLab/pimms
Imputing proteomics data using deep learning models
RasmussenLab/IntroToML
Machine Learning course at Copenhagen University, Faculty of Health and Medical Sciences, Center for Health Data Science
RasmussenLab/vCentenarian
Repository with supporting code and information for virome analysis of Centenarian metagenomes
RasmussenLab/hela_qc_mnt_data
Functionality for processing and analysing HeLa data quality control (QC) and maintenance (MNT) samples processed by MaxQuant
RasmussenLab/njab
Not just another biomarkers: Functionality for biomarker discovery
RasmussenLab/taxconverter
Unifies the presentation of taxonomic classifiers output
RasmussenLab/avamb
Variational autoencoder for metagenomic binning
RasmussenLab/CLASTER
Modeling nascent RNA transcription from chromatin landscape and structure
RasmussenLab/EIR
A toolkit for training deep learning models on genotype, tabular, sequence, image, array and binary data.
RasmussenLab/git-tutorial
VS Code on Binder
RasmussenLab/HAPI
Script to run HAPI (Haplotype-Aware Probabilistic model for Indels) to identify the CCR5delta32 deletion in ancient low coverage dna samples
RasmussenLab/paper_template
An Overleaf template for an initial journal submission and a preprint
RasmussenLab/python_package
Example Python package
RasmussenLab/cirrhosis_death
Predict who dies from newly diagnosed cirrhosis
RasmussenLab/decision-making-vaes
Decision based on models using the latent encoding (approx. posterior)
RasmussenLab/PSYCH-Pred
RasmussenLab/PSYCH-VAE
RasmussenLab/PythonTsunami
RasmussenLab/avamb-1
Variational autoencoder for metagenomic binning
RasmussenLab/cr2_usage_bot
Send cr2 usage on computerome to a Slack channel.
RasmussenLab/extract_genotypes_workflow
Call and encode genotypes based on a selection of positions.
RasmussenLab/list_your_publications
Query your list of publications using ORCID and DOI APIs
RasmussenLab/pan_uk_biobank_gwas_snps
RasmussenLab/thesis_template
Template of thesis for overleaf at Copenhagen University
RasmussenLab/thesis_template_sund
PhD thesis template using styling from the faculty of health and medical sciences (short SUND in Danish)
RasmussenLab/VAEs_for_biomedical_data_integration
This is the repository for the paper "On the use of VAEs for biomedical data integration".
RasmussenLab/vamb-aamb
Variational and Adversarial autoencoders for metagenomic binning