79 citations · 92 across the 4 of their papers we have counts for
4 papers
How to make the most of your masked language model for protein engineering
Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober +1
A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological pr…
Improved Therapeutic Antibody Reformatting through Multimodal Machine Learning
Jiayi Xin, Aniruddh Raghu, Nick Bhattacharya +3
Modern therapeutic antibody design often involves composing multi-part assemblages of individual functional domains, each of which may be derived from a different source or enginee…
Evaluating Protein Transfer Learning with TAPE
Roshan Rao, Nicholas Bhattacharya, Neil Thomas +5
Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cos…
Time-Inhomogeneous Branching Processes Conditioned on Non-Extinction
Nicholas Bhattacharya, Mark Perlman
In this paper, we consider time-inhomogeneous branching processes and time-inhomogeneous birth-and-death processes, in which the offspring distribution and birth and death rates (r…