held in conjunction with

ICCS 20223

(INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE)

Prague, Czech Republic

3-5 July, 2023

Networks are present in different aspects of our life: communication networks, World Wide Web, Social Networks, and can be used to conveniently describe biological and clinical data, such as the interactions of proteins in an organisms or the connections of neurons in the brain. Therefore, network science, focusing on the network representations of physical, biological and social phenomena and leading to predictive models of these phenomena, currently represents a vast field of application and research for many scientific and social disciplines. The mathematical background for the study and analysis of networks has its roots in the theory of graphs, that allows studying real phenomena in a quantitative way. According to the formalism coming from graph-theory, nodes of the graph represent entities, while edges represent the associations among them.

Currently, in bioinformatics and systems biology, there is a growing interest in analysing associations among biological molecules at a network level. Since the study of associations in a system-level scale has shown great potential, the use of networks has become the de-facto standard for representing such associations, and its application fields span from molecular biology to brain connectome analysis. Molecules of different types, e.g., genes, proteins, ribonucleic acids, and metabolites, have fundamental roles in the mechanisms of the cellular processes. The study of their structure and interactions is crucial for different reasons, comprising the development of new drugs and the discovery of disease pathways.

Thus, the modelling of the complete set of interactions and associations among biological molecules as a graph is convenient for a variety of reasons. Networks provide a simple and intuitive representation of heterogeneous and complex biological processes. Moreover, they facilitate modelling and understanding of complicated molecular mechanisms combining graph theory, machine learning and deep learning techniques. In general, complex biological systems represented as networks, provide an integrated way to look into the dynamic behaviour of the cellular system through the interactions of components. In computational biology, homogeneous networks have been used to model interactions among single type of biological macromolecules inside cells, such as protein-protein interactions (PPI), or gene-gene interactions. Also, heterogeneous networks are used to model the interplay of molecules of different types, e.g., genes, proteins and ribonucleic acids) that represent constitutive blocks of mechanisms inside cells, by using nodes and edge of different types, (i.e. implemented as node/ edge-coloured graphs). Special cases of heterogeneous networks are multilayer networks.

The multilayer network model is widely used as a powerful tool to represent the organization and relationships of complex data in many domains. Multi- layer networks, which initially gained momentum in social computing, are designed to provide a more realistic representation of the different and heterogeneous relations that may characterize an entity in the network system. Thus, networks and network analysis methods are a keystone in computational biology and bioinformatics and are increasingly used to study biological and clinical data in an integrated way.

TOPICS OF INTEREST

The workshop is seeking original research papers presenting applications of parallel and high performance computing to biology and medicine. Topics of interest include, but are not limited to:

• Network-based bioinformatics methods;

• Networks-based applications in computational biology, genomics, medicine, and healthcare;

• Graph representation learning for visualizing and interpreting biological and biomedical data;

• Bioinformatics methods for network-based analysis and visualization;

• Network-based modelling and analysis of complex diseases;

• Complex network models for structure and function analysis;

• Network models in epidemiology;

• Next-generation network science;

• Artificial intelligence for network models of complex diseases;

• Applications of deep learning approaches in computational biology, genomics, medicine, and healthcare;

• Networks Alignment;

• Complex Prediction;

• Network Embedding;

• Pathways Analysis;

• Interactomics databases;

• Pathways databases.

INTEREST TO ICCS COMMUNITY

Biological Network Modelling and Analysis can be utilized in several applications such as the identification of drug targets, determining the role of proteins or genes of unknown function, the design of effective strategies for infectious diseases and the early diagnosis of neurological disorders through detecting abnormal patterns of neural synchronization in specific brain regions.

The main motivation for the workshop is focuses on the collection of advanced works on development of new pipelines, algorithms and tools for the network analysis of complex systems in different domains.