71 citations · 140 across the 6 of their papers we have counts for
6 papers
Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data
Robert Frank, Michael Widrich, Rahmad Akbar +4
Generative machine learning models offer a powerful framework for therapeutic design by efficiently exploring large spaces of biological sequences enriched for desirable properties…
Progress and Challenges for the Application of Machine Learning for Neglected Tropical Diseases
Chung Yuen Khew, Rahmad Akbar, Norfarhan Mohd. Assaad
Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long exi…
ImmunoLingo: Linguistics-based formalization of the antibody language
Mai Ha Vu, Philippe A. Robert, Rahmad Akbar +4
Apparent parallels between natural language and biological sequence have led to a recent surge in the application of deep language models (LMs) to the analysis of antibody and othe…
Linguistically inspired roadmap for building biologically reliable protein language models
Mai Ha Vu, Rahmad Akbar, Philippe A. Robert +4
Deep neural-network-based language models (LMs) are increasingly applied to large-scale protein sequence data to predict protein function. However, being largely black-box models a…
AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation
Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit +12
Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody domi…
Augmenting adaptive immunity: progress and challenges in the quantitative engineering and analysis of adaptive immune receptor repertoires
Alex J. Brown, Igor Snapkov, Rahmad Akbar +4
The adaptive immune system is a natural diagnostic and therapeutic. It recognizes threats earlier than clinical symptoms manifest and neutralizes antigen with exquisite specificity…