Graph representation learning in biomedicine and healthcare
Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …
biomedicine and healthcare, they can represent, for example, molecular interactions …
A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
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 …
Comprehensive survey of recent drug discovery using deep learning
Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …
significantly reduces the time and cost required for developing novel drugs. With the …
A benchmark study of deep learning-based multi-omics data fusion methods for cancer
D Leng, L Zheng, Y Wen, Y Zhang, L Wu, J Wang… - Genome biology, 2022 - Springer
Background A fused method using a combination of multi-omics data enables a
comprehensive study of complex biological processes and highlights the interrelationship of …
comprehensive study of complex biological processes and highlights the interrelationship of …
[HTML][HTML] Protein–protein interaction prediction with deep learning: A comprehensive review
Most proteins perform their biological function by interacting with themselves or other
molecules. Thus, one may obtain biological insights into protein functions, disease …
molecules. Thus, one may obtain biological insights into protein functions, disease …
A distributed framework for large-scale protein-protein interaction data analysis and prediction using mapreduce
Protein-protein interactions are of great significance for human to understand the functional
mechanisms of proteins. With the rapid development of high-throughput genomic …
mechanisms of proteins. With the rapid development of high-throughput genomic …
Incorporating machine learning into established bioinformatics frameworks
N Auslander, AB Gussow, EV Koonin - International journal of molecular …, 2021 - mdpi.com
The exponential growth of biomedical data in recent years has urged the application of
numerous machine learning techniques to address emerging problems in biology and …
numerous machine learning techniques to address emerging problems in biology and …
Knowledge graphs and their applications in drug discovery
F MacLean - Expert opinion on drug discovery, 2021 - Taylor & Francis
Introduction Knowledge graphs have proven to be promising systems of information storage
and retrieval. Due to the recent explosion of heterogeneous multimodal data sources …
and retrieval. Due to the recent explosion of heterogeneous multimodal data sources …
DeepDISOBind: accurate prediction of RNA-, DNA-and protein-binding intrinsically disordered residues with deep multi-task learning
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many
IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported …
IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported …