This Research Topic is Part 2 of the successful Topic
"Graph-Based Methods Volume I: Graph-Based Learning and its applications for Biomedical Data Analysis" previously launched in
Frontiers in Genetics.
Driven by the varieties of applications in medical informatics and molecular biology, vast amounts of biomedical data have been accumulated in the past decades. The 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, many innovative graph-based methods such as graph representation learning, graph-based inference, and graph neural network have been proposed, and they have demonstrated the great potential 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 innovative graph-based methods and theories that focus on the analysis of structured biomedical data, solving problems of wide interest such as drug-drug interaction prediction, drug-disease association prediction, protein-protein interaction prediction, drug-target interaction prediction, lncRNA-disease association prediction, etc. Papers are solicited that address theoretical as well as practical issues related to the Research Topic's theme.
Topics of interest include (but are not limited to):
1) Graph-based inference for biomedical data analysis
2) Graph representation learning for biomedical data analysis
3) Graph-based deep learning for biomedical data analysis
4) Knowledge graph-based biomedical data analysis
5) Graph-based classification for biomedical data analysis
6) Graph-based clustering for biomedical data analysis
This Research Topic is Part 2 of the successful Topic
"Graph-Based Methods Volume I: Graph-Based Learning and its applications for Biomedical Data Analysis" previously launched in
Frontiers in Genetics.
Driven by the varieties of applications in medical informatics and molecular biology, vast amounts of biomedical data have been accumulated in the past decades. The 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, many innovative graph-based methods such as graph representation learning, graph-based inference, and graph neural network have been proposed, and they have demonstrated the great potential 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 innovative graph-based methods and theories that focus on the analysis of structured biomedical data, solving problems of wide interest such as drug-drug interaction prediction, drug-disease association prediction, protein-protein interaction prediction, drug-target interaction prediction, lncRNA-disease association prediction, etc. Papers are solicited that address theoretical as well as practical issues related to the Research Topic's theme.
Topics of interest include (but are not limited to):
1) Graph-based inference for biomedical data analysis
2) Graph representation learning for biomedical data analysis
3) Graph-based deep learning for biomedical data analysis
4) Knowledge graph-based biomedical data analysis
5) Graph-based classification for biomedical data analysis
6) Graph-based clustering for biomedical data analysis
