Artificial Intelligence (AI) has been increasingly adopted to a wide range of biological areas such as drug target identification, systems biology, pharmacogenomics, network pharmacology, chemical property prediction, synthesis planning, molecular design and generation, protein-ligand interaction, drug-target interaction network, drug-related knowledge graphs, big data analysis for drug information, and image recognition. The practice of AI in biological areas faces challenges of the integration of biological resources which is usually multi-modal, intertwined, and not guaranteed to be consistent.
Based on the experimental and empirical criteria, a lot of AI practices on biological resources are successful in terms of speeding up, improving the success rates, and lower the cost of drug discovery and drug development, etc. However, most of the practices cannot provide a theoretical explanation for improved accuracy, enhanced robustness, and thereafter ensure the reproducibility on changed data samples. With the accelerated integration of various sensor facilities, especially Internet of Things (IoT) and instrumental equipment at various degrees of details and depths, integrated processing of hybrid biological resources covering data, information and knowledge originating in clinical, genetic, genomic, proteomic sources has been increasingly necessary, available and affordable, towards achieving improved accuracy and precision. Various integration efforts have been devoted in building capabilities of integration of multi-modal data to retrieve information, abstraction on information to attain knowledge hypotheses, and crossing optimization. Integrated resources provides opportunities to achieve more precise data reproducibility through multiple sources sharing while avoiding repeated collection, more targeted data reproducibility through sharing multiple sources of resources while avoiding repeated operations, more efficient information reproducibility through interconnection of multiple sources of resources while avoiding repeated synthesis, and more comprehensive knowledge reproducibility through multiple sources reasoning and abstraction. These trends further increases the challenging of providing explainable and interactive AI solutions.
Prevailing challenges towards the solution arise for both conceptual foundations and technical preparation, especially involving semantic understanding and utilization. As the Knowledge Graphs are increasingly recognized as an important approach to solving problems related to semantic understanding beyond question and answering systems, various solutions focusing on Knowledge Graphs have been proposed. These efforts cover Knowledge Graphs creation, understanding, searching, reasoning, modification and especially and most recently embedding technologies with Machine Learning. A foreseeable AI landscape with explainable and interactive human interactions is becoming feasible based on Knowledge Graphs. The boundaries of the capability of Knowledge Graphs usages are constantly expanding, but there are also open questions as to what issues can be solved by Knowledge Graphs alone. Hierarchical architectures in which project Data, Information, Knowledge, and Wisdom (DIKW) seem to be well paired with the organizing capability of Knowledge Graph technologies in terms of the 5W ( What, Where, When, How and Why). Although not yet formally settled as a uniform concept itself, Knowledge Graphs have been actually or implicitly functioning as Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph according to the DIKW hierarchy. This Research Topic aims to address experimental and theoretical results towards explainable intelligent processing of biological resources crosscutting or integrating data, information, knowledge, and wisdom.
We welcome Original Research and Review articles covering topics of interest include but are not limited to:
- Latest machine learning algorithms with applications on graphical biomedicine and bioinformatics
- Graphical content analysis techniques for biomedicine and bioinformatics
- Explainable machine learning in biomedicine and bioinformatics
- Knowledge creation and management in biomedicine and bioinformatics
- Ontology modeling for biological information retrieve
- Multi-modal biological data collections and annotation
- Knowledge based signal/image/video/text feature extraction
- Knowledge creation and reasoning for biological processing
- DIKW architecture-based biological resource processing and service provision
- Ontological approach and formal modeling, and verification methods of graphical biological resources
- Multi objective graphical optimization with an application on biomedicine
Artificial Intelligence (AI) has been increasingly adopted to a wide range of biological areas such as drug target identification, systems biology, pharmacogenomics, network pharmacology, chemical property prediction, synthesis planning, molecular design and generation, protein-ligand interaction, drug-target interaction network, drug-related knowledge graphs, big data analysis for drug information, and image recognition. The practice of AI in biological areas faces challenges of the integration of biological resources which is usually multi-modal, intertwined, and not guaranteed to be consistent.
Based on the experimental and empirical criteria, a lot of AI practices on biological resources are successful in terms of speeding up, improving the success rates, and lower the cost of drug discovery and drug development, etc. However, most of the practices cannot provide a theoretical explanation for improved accuracy, enhanced robustness, and thereafter ensure the reproducibility on changed data samples. With the accelerated integration of various sensor facilities, especially Internet of Things (IoT) and instrumental equipment at various degrees of details and depths, integrated processing of hybrid biological resources covering data, information and knowledge originating in clinical, genetic, genomic, proteomic sources has been increasingly necessary, available and affordable, towards achieving improved accuracy and precision. Various integration efforts have been devoted in building capabilities of integration of multi-modal data to retrieve information, abstraction on information to attain knowledge hypotheses, and crossing optimization. Integrated resources provides opportunities to achieve more precise data reproducibility through multiple sources sharing while avoiding repeated collection, more targeted data reproducibility through sharing multiple sources of resources while avoiding repeated operations, more efficient information reproducibility through interconnection of multiple sources of resources while avoiding repeated synthesis, and more comprehensive knowledge reproducibility through multiple sources reasoning and abstraction. These trends further increases the challenging of providing explainable and interactive AI solutions.
Prevailing challenges towards the solution arise for both conceptual foundations and technical preparation, especially involving semantic understanding and utilization. As the Knowledge Graphs are increasingly recognized as an important approach to solving problems related to semantic understanding beyond question and answering systems, various solutions focusing on Knowledge Graphs have been proposed. These efforts cover Knowledge Graphs creation, understanding, searching, reasoning, modification and especially and most recently embedding technologies with Machine Learning. A foreseeable AI landscape with explainable and interactive human interactions is becoming feasible based on Knowledge Graphs. The boundaries of the capability of Knowledge Graphs usages are constantly expanding, but there are also open questions as to what issues can be solved by Knowledge Graphs alone. Hierarchical architectures in which project Data, Information, Knowledge, and Wisdom (DIKW) seem to be well paired with the organizing capability of Knowledge Graph technologies in terms of the 5W ( What, Where, When, How and Why). Although not yet formally settled as a uniform concept itself, Knowledge Graphs have been actually or implicitly functioning as Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph according to the DIKW hierarchy. This Research Topic aims to address experimental and theoretical results towards explainable intelligent processing of biological resources crosscutting or integrating data, information, knowledge, and wisdom.
We welcome Original Research and Review articles covering topics of interest include but are not limited to:
- Latest machine learning algorithms with applications on graphical biomedicine and bioinformatics
- Graphical content analysis techniques for biomedicine and bioinformatics
- Explainable machine learning in biomedicine and bioinformatics
- Knowledge creation and management in biomedicine and bioinformatics
- Ontology modeling for biological information retrieve
- Multi-modal biological data collections and annotation
- Knowledge based signal/image/video/text feature extraction
- Knowledge creation and reasoning for biological processing
- DIKW architecture-based biological resource processing and service provision
- Ontological approach and formal modeling, and verification methods of graphical biological resources
- Multi objective graphical optimization with an application on biomedicine
