Genome-wide association studies (GWAS) have become indispensable in human medicine
and genomics, but very few have been carried out on bacteria. Here we introduce Scoary, …

Generative machine learning (ML) has been postulated to become a major driver in the
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope
interface. The predictability of antibody-antigen binding is a prerequisite for de novo …

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they
record past and ongoing adaptive immune responses. The capacity of machine learning (…

Brucellosis is a rarely encountered infection in Norway. The aim of this study was to explore
all Brucella melitensis isolates collected in Norway from 1999 to 2016 in relation to origin of …

Abstract Machine learning (ML) is a key technology for accurate prediction of antibody–antigen
binding. Two orthogonal problems hinder the application of ML to antibody-specificity …

The process of recombination between variable (V), diversity (D), and joining (J) immunoglobulin
(Ig) gene segments determines an individual's naive Ig repertoire and, consequently, (…

Background Machine learning (ML) methodology development for the classification of
immune states in adaptive immune receptor repertoires (AIRRs) has seen a recent surge of …

Abstract Machine learning (ML) is a key technology to enable accurate prediction of
antibody-antigen binding, a prerequisite for in silico vaccine and antibody design. Two orthogonal …

Background Machine learning (ML) has gained significant attention for classifying immune
states in adaptive immune receptor repertoires (AIRRs) to support the advancement of …