Lonneke Scheffer

The paper revises the standardization and validation pipeline for immune receptor data in the IEDB to improve data consistency and usability.

This paper introduces immuneML, a framework for unsupervised machine learning in adaptive immune receptor repertoires, demonstrating its use in generative modeling, clustering, and dataset analysis.

The paper identifies TCR subsequence motifs associated with type 1 diabetes using deep learning and shows their enrichment in T1D individuals based on HLA risk alleles.

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

CompAIRR rapidly compares adaptive immune receptor repertoires using exact or approximate sequence matching, significantly improving speed and memory efficiency compared to existing methods.

The paper evaluates ML models for adaptive immune receptor repertoire classification, finding that simple methods like L1-penalized logistic regression perform well even with rare immune signals.

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.