A guide to machine learning for biologists
The expanding scale and inherent complexity of biological data have encouraged a growing
use of machine learning in biology to build informative and predictive models of the …
use of machine learning in biology to build informative and predictive models of the …
[HTML][HTML] AlphaFold and implications for intrinsically disordered proteins
Accurate predictions of the three-dimensional structures of proteins from their amino acid
sequences have come of age. AlphaFold, a deep learning-based approach to protein …
sequences have come of age. AlphaFold, a deep learning-based approach to protein …
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 …
Evolutionary-scale prediction of atomic-level protein structure with a language model
Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …
sequence alignments to predict protein structure. We demonstrate direct inference of full …
[PDF][PDF] Language models of protein sequences at the scale of evolution enable accurate structure prediction
Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …
scale, going beyond simple pattern matching to perform higher level reasoning and …
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 …
Efficient evolution of human antibodies from general protein language models
Natural evolution must explore a vast landscape of possible sequences for desirable yet
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
[HTML][HTML] Progen2: exploring the boundaries of protein language models
Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …
success at classification and generation tasks relevant for artificial-intelligence-driven …
Language models enable zero-shot prediction of the effects of mutations on protein function
Modeling the effect of sequence variation on function is a fundamental problem for
understanding and designing proteins. Since evolution encodes information about function …
understanding and designing proteins. Since evolution encodes information about function …