Rahmad Akbar

Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability

The paper quantifies the plasticity of antibody developability by analyzing sequence and structure-based parameters in natural and human-engineered antibodies.

Simple computational methods can outperform deep learning in designing diverse, binder-enriched antibody libraries

The paper shows that simple computational methods like BLOSUM can create diverse, binder-enriched antibody libraries better than deep learning approaches.

Reference-based comparison of adaptive immune receptor repertoires

The paper introduces immuneREF, a method to quantify immune repertoire similarity across health and disease, finding that healthy and diseased repertoires are more similar than previously thought.

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

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.

immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking

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