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 …
Bioinformatics approaches to discovering food-derived bioactive peptides: Reviews and perspectives
Food-derived bioactive peptides (FBPs) are gaining interest due to their great potential in
agricultural byproduct valorization and high-activity peptide screening. The introduction of …
agricultural byproduct valorization and high-activity peptide screening. The introduction of …
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 …
NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning
Recent advances in machine learning and natural language processing have made it
possible to profoundly advance our ability to accurately predict protein structures and their …
possible to profoundly advance our ability to accurately predict protein structures and their …
Transformer protein language models are unsupervised structure learners
Unsupervised contact prediction is central to uncovering physical, structural, and functional
constraints for protein structure determination and design. For decades, the predominant …
constraints for protein structure determination and design. For decades, the predominant …
Contrastive learning in protein language space predicts interactions between drugs and protein targets
Sequence-based prediction of drug–target interactions has the potential to accelerate drug
discovery by complementing experimental screens. Such computational prediction needs to …
discovery by complementing experimental screens. Such computational prediction needs to …
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Predicting the functional sites of a protein from its structure, such as the binding sites of small
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …
PredictProtein-predicting protein structure and function for 29 years
Abstract Since 1992 PredictProtein (https://predictprotein. org) is a one-stop online resource
for protein sequence analysis with its main site hosted at the Luxembourg Centre for …
for protein sequence analysis with its main site hosted at the Luxembourg Centre for …
TITAN: T-cell receptor specificity prediction with bimodal attention networks
Motivation The activity of the adaptive immune system is governed by T-cells and their
specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent …
specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent …