Milena Pavlović

TL;DR: A new PyTorch layer is provided, called "Hopfield", which allows to equip deep learning architectures with modern Hopfield networks as a new powerful concept comprising pooling, memory, and attention.

TL;DR: In this paper, the authors identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions, and show that this vocabulary enables the machine learnability of antibody-antigen binding using generative machine learning.

TL;DR: The adaptive immune system is a natural diagnostic sensor and therapeutic that can be used as a diagnostic and therapeutic tool in the medical field.

TL;DR: The developed immuneSIM package permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection.

TL;DR: In this article, the authors highlight advances, challenges and future directions of quantitative approaches which seek to advance the fundamental understanding of immunological phenomena, and reverse engineer the immune system to produce auspicious biopharmaceutical drugs and immunodiagnostics.