Multi‐omics usually refers to the crossover application of multiple high‐throughput screening
technologies represented by genomics, transcriptomics, single‐cell transcriptomics …

High rates of failure, exorbitant costs, and the sluggish pace of new drug discovery and
development have led to a growing interest in repurposing “old” drugs to treat both common …

Develo** personalized diagnostic strategies and targeted treatments requires a deep
understanding of disease biology and the ability to dissect the relationship between …

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 …

Changes in gut microbiota have been associated with several diseases. Here, the
International Multiple Sclerosis Microbiome Study (iMSMS) studied the gut microbiome of …

Implementing precision medicine hinges on the integration of omics data, such as
proteomics, into the clinical decision-making process, but the quantity and diversity of …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Computational drug repurposing aims to identify new indications for existing drugs by
utilizing high-throughput data, often in the form of biomedical knowledge graphs. However …

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various
areas, such as virtual screening, drug repurposing and identification of potential drug side …

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …