Symposium B4 “Big biological data analysis, ontologies and artificial intelligence”

This symposium is an interdisciplinary forum for presenting and discussing the latest advances in areas such as: analysis of large-scale genetic/biological data using artificial intelligence methods; systems methodology and theory; and ontological representation and modeling of subject domains.

The symposium’s central topics include:

• automated extraction of knowledge and facts from Big genetic datasets presented in scientific publications, patents, and factographic databases, using machine reading and artificial intelligence methods;
• integration of bioinformatics/systems computational biology methods with artificial intelligence/machine learning approaches for analyzing Big genetic/biological data and for modeling genetic/biological systems and processes;
• interpretability, explainability, and substantive validity of AI-derived decisions/inferences obtained in the analysis of large genetic/biological data;
• application of artificial intelligence/machine learning methods to problems in the domains of “Genetics,” “Medicine,” “Biotechnology,” “Pharmacology,” “Agrobiotechnologies,” and other life-science–related fields;
• ontological description and modeling of subject domains enabling efficient information retrieval and analysis;
• methods of explainable and causal artificial intelligence (generative AI and large language models (LLMs), graph neural networks (GNNs), deep neural networks (DNNs), transformers, Kolmogorov–Arnold networks (KAN), etc.) for analysis and modeling of the structural-functional organization and dynamics of complex systems and processes in genetics and the life sciences (genomes, biological macromolecules, gene networks, metabolic pathways, functional systems of organisms, populations, ecosystems, etc.);
• algorithms and computational tools for deriving meaningful conclusions from large-scale genetic (biological) data (scalability, data quality, integration, validation);
• development of causal artificial intelligence methods for effective investigation of cause-and-effect relationships in complex systems/processes;
• mathematical and methodological problems of systems theory (including functional systems): basic elements and modules, hierarchical structure, integrity, synergetics, and system-forming factors.