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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

Cython bindings and Python interface to SWORD (Smith Waterman On Reduced Database), a method for fast database search.

This source code extracts distributions of nups inside core and non-core regions; and was developed to quantify different nups for the manuscript below: Otsuka "A quantitative map of nuclear pore assembly reveals two distinct mechanisms"