About this Research Topic
Driven by the variety of applications in medical informatics and molecular biology, vast amounts of biomedical data have been accumulated in the past decades. Biomedical data are characterized by collections of interrelated objects, linked together into complex graphs and structures. These complex structures bring us challenges for the analysis of biomedical data. Recently, graph-based learning demonstrates the great advantages in analyzing complex biomedical graphs derived from biomedical data.
The goal of this Research Topic is to bring together high-quality papers that exploit the usage of graph-based learning approaches that focus on the analysis of structured biomedical data, such as drug-drug interaction prediction, protein-protein prediction, drug-target prediction, and long non-coding RNA- (lncRNA) disease association prediction. We aim to appeal to researchers in biomedical data analysis who are making non-trivial use of graph-based learning and their underlying theory. Papers are solicited that address theoretical as well as practical issues related to the Research Topic theme. Topics of interest include (but are not limited to):
1) Graph-based inferences for biomedical problems
2) Graph representation learning for biomedical problems
3) Deep learning in biomedical graphs
4) Graph learning and clustering in biomedical networks
5) Biomedical graph matching
Keywords: Graph embedding, Graph representation learning, Graph inference
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.