Mathematical methods and high-performance computing in the life sciences, biomedicine and biotechnology

1. Section “Mathematical methods of bioinformatics and systems biology“
   In this section we consider all topics that combine mathematics, computer science, informatics and artificial intelligence to model, analyze and interpret biological and medical data. In particular: identification of candidates genes, sequence alignment, gene finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein–protein interactions, mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences.

 

2. Section “High Performance and Distributed Computing in Life Sciences”;

In this section, various applications of modern numerical techniques to actual problems of bioinformatics, genomics, molecular biology, systems biology, biophysics, biochemistry, cell biology, population biology, ecology, neuroscience, and immunology are discussed. It seeks high-quality original contributions, which demonstrate the theoretical and practical progress of High Performance and Distributed Computing as well as implementation experiences from researchers in the area. It will focus on the use of up-to-date high-performance computing methods, new generation of high-performance computers and grid technologies, advances in numerical mathematics, computational statistics, ill-posed and inverse problems, numerical solving of the systems of ordinary and stochastic differential equations, parallel algorithms and so on.

 

3. Machine Learning and the ontological description of living systems;

In this section, topics covering issues/applications of machine learning in biology and medicine will be included, but not limited to: supervised, semi-supervised, unsupervised and reinforcement learning from biological and medical data; big data and machine learning algorithms for health-care; deep learning algorithms for classification of medical images, genome analysis and drug discovery; machine learning software used in biology and medicine.

Biology and medicine are one of the broadest sources of terminological knowledge given in the form of vocabularies, taxonomies, thesauri, ontologies. Formal concept systems are used in various information processing and integration tasks  such as, e.g., annotation and curation of experimental data, publication search, data source mapping, etc. Moreover, they represent background knowledge, which can be employed in machine learning tools as a part of a model architecture or in training to obtain more accurate and explainable results. This section covers a broad range of topics related to ontology engineering including but not limited to: methods and tools to support ontology lifecycle, ontology building, versioning, (de)composition, and mapping, use cases and applications of ontologies, ontology-driven biomedical information systems, applications of ontologies in data mining and machine learning for biomedical disciplines.

4. Mathematical modeling of biological systems and processes;

The main topics of this section are related to modeling of population dynamics, epidemic expansions, and gene networks functioning by means of statistical methods and systems of differential equations, in particular by equations with delayed arguments and stochastic equations. Description of phase portraits of these systems, questions of existence, (non)uniqueness of stability of oscillating solutions, inverse problems of identification of parameters in these models, interpretation of results of this modeling will be in the focus of reports of this section

5. Applied tasks of bioinformatics and systems biology

In this section software systems, cloud services including complex data processing scenarios aimed at solving applications of bioinformatics, computer pharmacology, biomedicine, biotechnology etc. will be presented. Both leading research centers and commercial companies – software developers in this areas are expected to participate.