Victor Greiff

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.

The paper identifies 48 novel light chain variable gene polymorphisms in a Norwegian cohort and finds partial intron retention in alternative splice variants of low-expressed genes.

Transformer attention is recast as the update rule of modern Hopfield networks, and the resulting DeepRC classifier handles immune repertoires with enormous instance counts and few informative sequences.

This study examines how different computational methods affect the analysis of B cell evolution in murine antibody repertoire sequencing data post-viral infection.

immuneSIM simulates B- and T-cell receptor repertoires with tunable features to benchmark immunoinformatics tools.

This study uses large-scale network analysis to reveal three fundamental principles of antibody repertoire architecture: reproducibility, robustness, and redundancy.

The paper identifies high-dimensional immunogenomic features that predict public and private antibody clones in humans and mice with 80% accuracy using machine learning.