The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. - Milena Pavlovic; Lonneke Scheffer; Keshav Motwani; Chakravarthi Kanduri; Radmila Kompova; Nikolay Vazov; Knut Waagan; Fabian L. M. Bernal; Alexandre Almeida Costa; Brian Corrie; Rahmad Akbar; Ghadi S. Al Hajj; et al. (Nat. Mach. Intell., 2021)

Ten simple rules for quick and dirty scientific programming. - Gabriel Balaban; Ivar Grytten; Knut Dagestad Rand; Lonneke Scheffer; Geir Kjetil Sandve (PLoS Comput. Biol., 2021)

TCRpower: quantifying the detection power of T-cell receptor sequencing with a novel computational pipeline calibrated by spike-in sequences. - Shiva Dahal-Koirala; Gabriel Balaban; Ralf Stefan Neumann; Lonneke Scheffer; Knut Erik Aslaksen Lundin; Victor Greiff; Ludvig Magne Sollid; Shuo-Wang Qiao; Geir Kjetil Sandve (Briefings Bioinform., 2022)

CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching. - Torbjørn Rognes; Lonneke Scheffer; Victor Greiff; Geir Kjetil Sandve (Bioinform., 2022)

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. - Philippe A. Robert; Rahmad Akbar; Robert Frank 006; Milena Pavlovic; Michael Widrich; Igor Snapkov; Andrei Slabodkin; Maria Chernigovskaya; Lonneke Scheffer; Eva Smorodina; Puneet Rawat; Brij Bhushan Mehta; et al. (Nat. Comput. Sci., 2022)

Predictability of antigen binding based on short motifs in the antibody CDRH3. - Lonneke Scheffer; Eric Emanuel Reber; Brij Bhushan Mehta; Milena Pavlovic; Maria Chernigovskaya; Eve Richardson; Rahmad Akbar; Fridtjof Lund-Johansen; Victor Greiff; Ingrid Hobæk Haff; Geir Kjetil Sandve (Briefings Bioinform., 2024)

Machine learning and computational analyses of adaptive immune receptors. - Lonneke Scheffer (2024)