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 …
A roadmap for metagenomic enzyme discovery
SL Robinson, J Piel, S Sunagawa - Natural Product Reports, 2021 - pubs.rsc.org
Covering: up to 2021 Metagenomics has yielded massive amounts of sequencing data
offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While …
offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While …
Highly accurate protein structure prediction for the human proteome
Protein structures can provide invaluable information, both for reasoning about biological
processes and for enabling interventions such as structure-based drug development or …
processes and for enabling interventions such as structure-based drug development or …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Learning functional properties of proteins with language models
Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …
uncharacterized properties of proteins; however, studies indicate that these methods should …
CATH: increased structural coverage of functional space
Abstract CATH (https://www. cathdb. info) identifies domains in protein structures from
wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and …
wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and …
Structure-based protein function prediction using graph convolutional networks
The rapid increase in the number of proteins in sequence databases and the diversity of
their functions challenge computational approaches for automated function prediction. Here …
their functions challenge computational approaches for automated function prediction. Here …
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Abstract Machine learning has been increasingly used for protein engineering. However,
because the general sequence contexts they capture are not specific to the protein being …
because the general sequence contexts they capture are not specific to the protein being …
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 …
Deep learning for ECG analysis: Benchmarks and insights from PTB-XL
Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its
interpretation is increasingly supported by algorithms. The progress in the field of automatic …
interpretation is increasingly supported by algorithms. The progress in the field of automatic …