Knowledge graphs: Opportunities and challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …
important to organize and represent the enormous volume of knowledge appropriately. As …
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 …
Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Large-scale multi-modal pre-trained models: A comprehensive survey
With the urgent demand for generalized deep models, many pre-trained big models are
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …
proposed, such as bidirectional encoder representations (BERT), vision transformer (ViT) …
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 foundation model for clinician-centered drug repurposing
Drug repurposing—identifying new therapeutic uses for approved drugs—is often a
serendipitous and opportunistic endeavour to expand the use of drugs for new diseases …
serendipitous and opportunistic endeavour to expand the use of drugs for new diseases …
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 …
A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
QA-GNN: Reasoning with language models and knowledge graphs for question answering
The problem of answering questions using knowledge from pre-trained language models
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …
(LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question …
Scientific large language models: A survey on biological & chemical domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …
natural language comprehension, representing a significant stride toward artificial general …