Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

Background: Determining drug–disease associations is an integral part in the process of
drug development. However, the identification of drug–disease associations through wet …

Drug repositioning is an efficient and promising strategy for traditional drug discovery and
development. Many research efforts are focused on utilizing deep-learning approaches …

In silico reuse of old drugs (also known as drug repositioning) to treat common and rare
diseases is increasingly becoming an attractive proposition because it involves the use of de …

Since the problem proposed in late 2000s, microRNA–disease association (MDA)
predictions have been implemented based on the data fusion paradigm. Integrating diverse …

Recent development of spatial transcriptomics (ST) is capable of associating spatial
information at different spots in the tissue section with RNA abundance of cells within each …

Drug repositioning is a promising drug development technique to identify new indications for
existing drugs. However, existing computational models only make use of lower-order …

Motivation: In recent years, a growing number of studies have proved that microRNAs
(miRNAs) play significant roles in the development of human complex diseases. Discovering …

Aspect-level sentiment analysis (ALSA) is the process of collecting, processing, analyzing,
inferring, and synthesizing subjective sentiments of entities contained in texts at the aspect …