Explore projects

R package to read, register and compare point sets from single molecule localization microscopy.

Rendered at: https://grp-bio-it.embl-community.io/template-pages-workbench/

This script implements a machine learning pipeline that integrates the ESM2 transformer-based protein model with a Logistic Regression classifier to predict the detectability of microprotein sequences by Mass Spectrometry. The script preprocesses data, extracts embeddings from sequences, performs feature selection, trains a logistic regression model, and evaluates its performance using multiple metrics.

Deployment configuration for the Bretzel App project.

Source code related to the publication "Visualization of loop extrusion by DNA nanoscale tracing in single human cells" by Beckwith, Ødegård-Fougner et.al. DOI: https://doi.org/10.1101/2021.04.12.439407

This project aims to offer a platform for sharing ideas, resources, tools & techniques, interesting publications, interviews and stories from all our community members. https://grp-bio-it.embl-community.io/blog/

These Python scripts implement binary classification models to predict detectability of noncanonical ORF microprotein sequences using i) a TensorFlow-Keras Classifier and ii) a Multi-Layer Perceptron (MLP) classifier to predict detectability of noncanonical ORF microprotein sequences derived 'HLA-predicted binding peptide sequences' from scikit-learn. The scripts preprocess the datasets, train an artificial neural network (ANN), evaluate performance using multiple metrics, and extract feature importance based on model weights.

The Bretzel app's front-end code.

Rendered at: https://grp-bio-it.embl-community.io/template-pages-mkdocs

train the trainer Rendered at: https://grp-bio-it.embl-community.io/template-pages-mkdocs

Rendered at: https://grp-bio-it.embl-community.io/template-pages-mkdocs