{"id":64,"date":"2019-08-06T09:02:59","date_gmt":"2019-08-06T06:02:59","guid":{"rendered":"https:\/\/bgrssb.icgbio.ru\/?page_id=64"},"modified":"2020-03-05T04:46:20","modified_gmt":"2020-03-05T04:46:20","slug":"mathematical-modeling-life-science","status":"publish","type":"page","link":"https:\/\/bgrssb.icgbio.ru\/2020\/mathematical-modeling-life-science\/","title":{"rendered":"Symposium “Mathematical methods and high-performance computing in the life sciences, biomedicine and biotechnology”"},"content":{"rendered":"

Mathematical methods and high-performance computing in the life sciences, biomedicine and biotechnology<\/h1>\n

Chairs<\/h3>\n

Sergey Goncharov<\/a><\/strong> <\/a>
\nSobolev Institute of Mathematics of SB RAS, Novosibirsk, Russia<\/p>\n

Andrey Palyanov<\/a><\/strong> <\/a>
\nA.P. Ershov Institute of Informatics Systems of SB RAS, Novosibirsk, Russia<\/p>\n

Marchenko Mikhail<\/a><\/strong> <\/a>
\nInstitute of Computational Mathematics and Mathematical Geophysics SB RAS,\u00a0Novosibirsk, Russia<\/p>\n

The symposium\u00a0“Mathematical methods in biology and medicine” will include the following research sections and meetings:<\/b><\/p>\n

\n
    \n
  1. Section \u201cMathematical methods of bioinformatics and systems biology\u201c
    \nIn 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\u2013protein interactions, mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences.<\/li>\n<\/ol>\n

     <\/p>\n

      \n
    1. Section \u201cHigh Performance and Distributed Computing in Life\u00a0Sciences\u201d;<\/li>\n<\/ol>\n

      In this section, various applications of modern numerical techniques to\u00a0actual problems of bioinformatics, genomics, molecular biology, systems\u00a0biology, biophysics, biochemistry, cell biology, population biology,\u00a0ecology, neuroscience, and immunology are discussed. It seeks high-quality\u00a0original contributions, which demonstrate the theoretical and practical progress of High Performance and Distributed Computing as well as\u00a0implementation experiences from researchers in the area. It will focus on\u00a0the use of up-to-date high-performance computing methods, new generation\u00a0of high-performance computers and grid technologies, advances in numerical\u00a0mathematics, computational statistics, ill-posed and inverse problems,\u00a0numerical solving of the systems of ordinary and stochastic differential\u00a0equations, parallel algorithms and so on.<\/p>\n

       <\/p>\n

        \n
      1. Machine Learning and the ontological description of living systems;<\/li>\n<\/ol>\n

        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.<\/p>\n

        Biology and medicine are one of the broadest sources of terminological knowledge given in the form of vocabularies,\u00a0taxonomies, thesauri, ontologies. Formal concept systems are used in various information processing and integration tasks\u00a0 such as, e.g., annotation and curation of experimental data, publication search, data source mapping, etc.\u00a0Moreover, they represent background knowledge, which can be employed in machine learning tools as a part\u00a0of a model architecture or in training to obtain more accurate and explainable results. This section covers a broad range\u00a0of topics related to ontology engineering including but not limited to: methods and tools to support ontology lifecycle,\u00a0ontology building, versioning, (de)composition, and mapping, use cases and applications of ontologies,\u00a0ontology-driven biomedical information systems, applications of ontologies in data mining and machine learning for biomedical disciplines.<\/p>\n

          \n
        1. Mathematical modeling of biological systems and processes;<\/li>\n<\/ol>\n

          The main topics of this section are related to modeling of population dynamics,\u00a0epidemic expansions, and gene networks functioning by means of statistical methods and systems\u00a0of differential equations, in particular by equations with delayed arguments and stochastic equations.\u00a0Description of phase portraits of these systems, questions of existence, (non)uniqueness of stability\u00a0of oscillating solutions, inverse problems of identification of parameters in these models,\u00a0interpretation of results of this modeling will be in the focus of reports of this section<\/p>\n

          5.\u00a0Applied tasks of bioinformatics and systems biology<\/p>\n

          In this section\u00a0software systems, cloud services\u00a0including complex data processing scenarios aimed at solving applications of bioinformatics, computer pharmacology, biomedicine, biotechnology etc. will be presented.\u00a0Both\u00a0<\/span>leading research centers and commercial companies \u2013 software developers in this areas are expected to participate.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

          Mathematical methods and high-performance computing in the life sciences, biomedicine and biotechnology Chairs Sergey Goncharov Sobolev Institute of Mathematics of SB RAS, Novosibirsk, Russia Andrey Palyanov A.P. Ershov Institute of Informatics Systems of SB RAS, Novosibirsk, Russia Marchenko Mikhail Institute of Computational Mathematics and Mathematical Geophysics SB RAS,\u00a0Novosibirsk, Russia The symposium\u00a0“Mathematical methods in biology and medicine” will include the following research sections and meetings: Section \u201cMathematical methods of bioinformatics and systems biology\u201c 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\u2013protein interactions, mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences.   Section \u201cHigh Performance and Distributed Computing in Life\u00a0Sciences\u201d; In this section, various applications of modern numerical techniques to\u00a0actual problems of bioinformatics, genomics, molecular biology, systems\u00a0biology, biophysics, biochemistry, cell biology, population biology,\u00a0ecology, neuroscience, and immunology are discussed. It seeks high-quality\u00a0original contributions, which demonstrate the theoretical and practical progress of High Performance and Distributed Computing as well as\u00a0implementation experiences from researchers in the area. It will focus on\u00a0the use of up-to-date high-performance computing methods, new generation\u00a0of high-performance computers and grid technologies, advances in numerical\u00a0mathematics, computational statistics, ill-posed and inverse problems,\u00a0numerical solving of the systems of ordinary and stochastic differential\u00a0equations, parallel algorithms and so on.   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,\u00a0taxonomies, thesauri, ontologies. Formal concept systems are used in various information processing and integration tasks\u00a0 such as, e.g., annotation and curation of experimental data, publication search, data source mapping, etc.\u00a0Moreover, they represent background knowledge, which can be employed in machine learning tools as a part\u00a0of a model architecture or in training to obtain more accurate and explainable results. This section covers a broad range\u00a0of topics related to ontology engineering including but not limited to: methods and […]<\/p>\n","protected":false},"author":9,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/pages\/64"}],"collection":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/comments?post=64"}],"version-history":[{"count":18,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/pages\/64\/revisions"}],"predecessor-version":[{"id":1450,"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/pages\/64\/revisions\/1450"}],"wp:attachment":[{"href":"https:\/\/bgrssb.icgbio.ru\/2020\/wp-json\/wp\/v2\/media?parent=64"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}