13 citations · 26 across the 4 of their papers we have counts for
5 papers
Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models
Isaac Ellmen, David Errington, Matthew I. J. Raybould +1
Although , here we show that is not equal to . Accordingly, the pa…
Effective Biological Representation Learning by Masking Gene Expression
Kian Kenyon-Dean, Alina Selega, Ihab Bendidi +5
RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. M…
Assessing interaction recovery of predicted protein-ligand poses
David Errington, Constantin Schneider, Cédric Bouysset +1
The field of protein-ligand pose prediction has seen significant advances in recent years, with machine learning-based methods now being commonly used in lieu of classical docking…
De novo antibody design with SE(3) diffusion
Daniel Cutting, Frédéric A. Dreyer, David Errington +2
We introduce IgDiff, an antibody variable domain diffusion model based on a general protein backbone diffusion framework which was extended to handle multiple chains. Assessing the…
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models
Daniel de Vassimon Manela, David Errington, Thomas Fisher +2
This paper proposes two intuitive metrics, skew and stereotype, that quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun r…