Bringing artificial intelligence on board Earth observation satellites unlocks unprecedented
possibilities to extract actionable items from various image modalities at the global scale in …

Introduction In recent years, graph neural network has been extensively applied to drug
discovery research. Although researchers have made significant progress in this field, there …

Identifying disease-associated microRNAs (miRNAs) could help understand the deep
mechanism of diseases, which promotes the development of new medicine. Recently …

Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets
through the analysis of drug–gene interactions. However, traditional experimental methods …

Common subspace learning methods only utilize local or global structure in feature
extraction, and cannot obtain the global optimal discriminative projection matrix. For this …

Drug–target interaction (DTI) identification is a complex process that is time-consuming,
costly and frequently inefficient, with a low success rate, especially with wet-experimental …

Identification of interactions between chemical compounds and proteins is crucial for various
applications, including drug discovery, target identification, network pharmacology, and …

The human microbiome plays a key role in drug development and precision medicine, but
understanding its complex interactions with drugs remains a challenge. Identifying microbe …

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis,
which can reveal the dynamic processes of biological development, including cell …

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of
cellular function and disease pathogenesis. Recent advances in single-cell RNA …