Kris Laukens

clusTCR is a Python tool for fast clustering of large CDR3 sequences, offering improved speed over existing methods while maintaining comparable accuracy.

This paper evaluates and adapts the CONSTANd normalization method for use with count data from sequencing, showing its efficiency and ability to remove bias across different experimental conditions.

The paper identifies challenges in predicting TCR-epitope interactions and introduces ImRex, a feature representation method that improves model performance for similar epitopes.

The paper shows Leishmania donovani lacks detectable DNA methylation, enabling enrichment of parasite DNA from mixed samples using methyl-binding columns.

MESSAR recommends metabolite substructures from tandem mass spectra without requiring predefined metabolite candidates.

TCRex identifies epitope-specific T cell receptor sequences in repertoires using machine learning and statistical analysis, validated on vaccine and infectious disease datasets.

The paper introduces an optimized version of the ANN-SoLo tool for faster and more accurate open modification spectral library searching using GPUs and feature hashing.

This paper introduces a tool that uses expert knowledge in machine learning to analyze dynamic metabolomics data, showing it performs as well as statistical methods.

The paper evaluates unsupervised clustering methods for T-cell receptor sequences and finds distinct clusters that target the same epitopes.

ANN-SoLo improves open modification spectral library searching by using approximate nearest neighbor indexing and a cascade search strategy, achieving faster speeds and more identifications than existing tools.

speaq 2.0 automates 1D NMR spectral processing and quantification, reducing user interaction and enabling reproducible metabolomics analysis.