Application of Artificial Intelligence Methods to Molecular Biology and Medicine
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (22 April 2022) | Viewed by 484
Special Issue Editors
Interests: cloud-based solutions for handling and analyzing smart city data; bioinformatics; semantic table understanding; graph data processing and visualization
Special Issues, Collections and Topics in MDPI journals
Interests: medical and biomedical image and signal processing; artificial intelligence; explainable artificial intelligence; digital twins; pattern recognition; data analysis; scientific visualization
Special Issues, Collections and Topics in MDPI journals
Interests: high-performance computing (heterogeneous, accelerated, large scale); machine learning; bioinformatics; personalized and precision medicine; image processing and compression; bio-medical imaging; network medicine, systems biology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In bioinformatics and biomedicine, many real, interesting problems, such as drug repurposing, network medicine approaches for subtype identification, protein function prediction, diagnoses, prognoses, and outcome prediction, can be modeled through knowledge graphs and knowledge networks. To conduct predictive, modeling, and analytics tasks on these graph-structured biological data, many intelligent tools are currently under development that purposely take advantage of relationships among entities. Such problems are characterized by ever-increasing graph sizes; therefore, these tools are required by design to be easily scalable when the network size grows fast, and should be equipped with intelligent user interfaces for supporting users in the visual analysis of the obtained results.
This Special Issue calls for innovative and easy-to-scale approaches in this domain. The application of novel machine learning and deep learning methods for the construction and analysis of large knowledge graphs in the context of bioinformatics and biomedicine, and the presentation of tools that support users in the visual exploration of their results, are particularly welcome.
The topics include, but are not limited to:
- Domain knowledge modeling and reasoning;
- Crowd-sourced knowledge graph construction methods;
- Knowledge graph data integration;
- Efficient and scalable approaches for working with knowledge graphs and biological networks;
- Intelligent user interfaces for the analysis of knowledge graphs;
- Link prediction in knowledge graphs;
- Large-scale graph-based prediction algorithms with applications in biomedicine and bioinformatics;
- Imbalanced learning algorithms working on knowledge graphs;
- Parallel/accelerated learning techniques for ultra-large knowledge graphs or networks;
- Intelligent user interfaces for the visual analysis of knowledge graphs.
Prof. Dr. Marco Mesiti
Prof. Dr. Elena Casiraghi
Dr. Alessandro Petrini
Guest Editors
Manuscript Submission Information
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Keywords
- domain knowledge modeling and reasoning
- crowd-sourced knowledge graph construction methods
- knowledge graph data integration
- efficient and scalable approaches for working with knowledge graphs and biological networks
- intelligent user interfaces for the analysis of knowledge graphs
- link prediction in knowledge graphs
- large-scale graph-based prediction algorithms with applications in biomedicine and bioinformatics
- imbalanced learning algorithms working on knowledge graphs
- parallel/accelerated learning techniques for ultra-large knowledge graphs or networks
- intelligent user interfaces for the visual analysis of knowledge graphs
