Cross-protein transfer learning substantially improves disease variant prediction
Background Genetic variation in the human genome is a major determinant of individual
disease risk, but the vast majority of missense variants have unknown etiological effects …
disease risk, but the vast majority of missense variants have unknown etiological effects …
Genome-wide prediction of disease variant effects with a deep protein language model
Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …
models have improved variant effect prediction accuracy, they cannot analyze all coding …
Disease variant prediction with deep generative models of evolutionary data
Quantifying the pathogenicity of protein variants in human disease-related genes would
have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of …
have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of …
High-throughput deep learning variant effect prediction with Sequence UNET
Understanding coding mutations is important for many applications in biology and medicine
but the vast mutation space makes comprehensive experimental characterisation …
but the vast mutation space makes comprehensive experimental characterisation …
Updated benchmarking of variant effect predictors using deep mutational scanning
BJ Livesey, JA Marsh - Molecular systems biology, 2023 - embopress.org
The assessment of variant effect predictor (VEP) performance is fraught with biases
introduced by benchmarking against clinical observations. In this study, building on our …
introduced by benchmarking against clinical observations. In this study, building on our …
Accurate proteome-wide missense variant effect prediction with AlphaMissense
The vast majority of missense variants observed in the human genome are of unknown
clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on …
clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on …
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations
BJ Livesey, JA Marsh - Molecular systems biology, 2020 - embopress.org
To deal with the huge number of novel protein‐coding variants identified by genome and
exome sequencing studies, many computational variant effect predictors (VEPs) have been …
exome sequencing studies, many computational variant effect predictors (VEPs) have been …
Predicting pathogenic protein variants
JA Marsh, SA Teichmann - Science, 2023 - science.org
Many of the genetic mutations that cause disease in humans occur in protein-coding
regions. Although the capacity to sequence DNA and identify these variants has …
regions. Although the capacity to sequence DNA and identify these variants has …
[HTML][HTML] Improved pathogenicity prediction for rare human missense variants
The success of personalized genomic medicine depends on our ability to assess the
pathogenicity of rare human variants, including the important class of missense variation …
pathogenicity of rare human variants, including the important class of missense variation …
Poet: A generative model of protein families as sequences-of-sequences
T Truong Jr, T Bepler - Advances in Neural Information …, 2023 - proceedings.neurips.cc
Generative protein language models are a natural way to design new proteins with desired
functions. However, current models are either difficult to direct to produce a protein from a …
functions. However, current models are either difficult to direct to produce a protein from a …