| Akhmetzianova L. | Design primers for LAMP – Amplification | 1.1 Genomics and transcriptomics of plants and animals |
| Babak O. | Relationship Patterns of the Genetic Regulation of Carotenoid and Flavonoid Accumulation in Tomato Fruits (S. lycopersicum) | 1.1 Genomics and transcriptomics of plants and animals |
| Kapusta A. | stLFR a Novel Approach to Genome Assembly | 1.1 Genomics and transcriptomics of plants and animals |
| Krasikova A. | Chicken oocyte transcriptome: RNAseq of nuclear and cytoplasmic long and short RNAs | 1.1 Genomics and transcriptomics of plants and animals |
| Maslov D. | Insights into Opisthorchis felineus genome organization. | 1.1 Genomics and transcriptomics of plants and animals |
| Petrushin I. | Diversity of CRISPR arrays identified into plant mitochondrial genomes | 1.1 Genomics and transcriptomics of plants and animals |
| Pronozin A. | ICAnnoLncRNA: automatic pipeline of identification, classification and annotation of long noncoding RNAs. | 1.1 Genomics and transcriptomics of plants and animals |
| Shevchenko A. | Post-stress changes in the gut microbiota in rat strains with different excitability of the nervous system. | 1.1 Genomics and transcriptomics of plants and animals |
| Vodiasova E. | Characterization of the glutathione S-transferase gene family in Mollusca | 1.1 Genomics and transcriptomics of plants and animals |
| Bezuglov V. | ITAS: integrated transcript annotation for small RNA | 1.2 Regulatory genomics |
| Gromyko D. | Promoter motif inference and annotation of promoter sequences in bacterial genomes based on the analysis of structures of alternative sigma factor-promoter complexes | 1.2 Regulatory genomics |
| Korobeynikova A. | Lymphocyte migration factors in breast cancer: a transcriptome analysis | 1.2 Regulatory genomics |
| Levitsky V. | Web-MCOT server for motifs co-occurrence search in ChIP-seq data | 1.2 Regulatory genomics |
| Matveenko A. | Amplification of the release factor genes as a mechanism of adaptation in yeast | 1.2 Regulatory genomics |
| Sergushichev A. | GESECA: Gene Set Co-regulation Analysis | 1.2 Regulatory genomics |
| Tsukanov A. | enRest tool for transcription factor binding sites overrepresentation analysis in RNA-seq data | 1.2 Regulatory genomics |
| Zhurakovskaya A. | Аctin genes of the White Sea sponge Halisarca dujardinii: structure and regulation | 1.2 Regulatory genomics |
| Kabirova E. | Influence of TADs boundary disruption on 3D genome organisation and genes expression at the mouse Pdgfra/Kit/Kdr locus | 1.3 Functional and applied 3D genomics |
| Lukyanchikova V. | Insights into the 3D-genome organization in malaria mosquitoes | 1.3 Functional and applied 3D genomics |
| Maslova A. | Chromosome structural variations of the chicken erythroblast HD3 cell line identified by Hi-C | 1.3 Functional and applied 3D genomics |
| Myakinkov I. | Development of a mESC line for studying the condensin II influence on interphase 3D genome organization | 1.3 Functional and applied 3D genomics |
| Ryumina E. | Higher order chromatin structure: new insight with novel correlative approach | 1.3 Functional and applied 3D genomics |
| Anashkina A. | Textual and Structural Analysis of RNA Polymerase II Core Promoter of Evolutionary Diverse Organisms | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Daugavet M. | Bacteriophages as vectors of gene transfer from prokaryotes to eukaryotes | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Gorshkova T. | Transcriptomics as an effective tool to study plant cell wall formation and function | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Kozlov A. | The carcino-evo-devo theory as a unified theory of biological developmen | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Lachynova M. | Development and application of a computational pipeline to analyze genes encoding phospholipase domains in flatworms | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Proskuryakova A. | Chromosome evolution in Ruminantia | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Pushmina E. | Analysis of environment adaptation features of the bacteria of Flavobacterium family by comparative genomics | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Saranchina A. | Genetic variety of Baikal endemic amphipods (Crustacea: Amphipoda) Eulimnogammarus verrucosus (Gerstf., 1858) in the Angara river | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Volobueva M. | Classification of prokaryotic DNA methyltransferases by sequence similarity of the catalytic domain | 1.4 Evolutionary genomics, bioinformatics, and molecular phylogeny |
| Khristichenko M. | Computation and tracing of periodic solutions of Marchuk-Petrov model | 2.1 Reconstruction, computational analysis and modeling of biological systems |
| Mustafin Z. | Evolutionary analysis of gene networks with Orthoweb software | 2.1 Reconstruction, computational analysis and modeling of biological systems |
| Neganova I. | Creation of the mathematical model for the prognostic analysis of human pluripotent stem cells based on their morphological portrait. | 2.1 Reconstruction, computational analysis and modeling of biological systems |
| Ivanisenko V. | ANDSystem: cognitive computer system for automated gene networks reconstruction and analysis | 2.1 Reconstruction, computational analysis and modeling of biological systems |
| Logofet D. | Alpine resort or battle for survival: A didactic story about short-lived perennials and their matrix models | 2.2 Modeling of population and ecological systems and processes |
| Shubin A. | Application of convolutional neural networks in the processing of hemispherical photos of the canopy | 2.2 Modeling of population and ecological systems and processes |
| Mishin A. | Overview of lipid-sensing GPCR receptors structure and function | 3.1 The role of synchrotron radiation and advanced instrumental techniques for macromolecular crystallography and pharmacology |
| Akimov S. | Statistical approach to regulation of membrane-permeabilizing activity of antimicrobial peptides | 3.2 Structural biology of proteins and membranes |
| Anashkina A. | Hierarchical Structure of Protein Sequences – New Insight of Protein Organization | 3.2 Structural biology of proteins and membranes |
| Efremov R. | “Dynamic molecular portraits” of biomembranes: a computational insight. | 3.2 Structural biology of proteins and membranes |
| Galzitskaya O. | Study of membrane permeability for various peptides by steered molecular dynamics | 3.2 Structural biology of proteins and membranes |
| Kuzmin A. | Structure and dynamics of the SARS-CoV-2 envelope protein | 3.2 Structural biology of proteins and membranes |
| Lyapina E. | Allosteric ligand subpocket of S1P5 as a determinant of inverse agonism and ligand specificity | 3.2 Structural biology of proteins and membranes |
| Sokolova O. | Use of lipodiscs in structural studies of ion channels | 3.2 Structural biology of proteins and membranes |
| Volynsky P. | Identification of factors important for the translocation of water-soluble proteins across the membrane using computer simulations | 3.2 Structural biology of proteins and membranes |
| Biziukova N. | Identification of virus-host interaction mechanisms based on text-mining approach | 3.3 Chemoinformatics and chemical biology |
| Karasev D. | Predicting the protein-ligand interactions based on ligands’ structures and proteins’ sequences | 3.3 Chemoinformatics and chemical biology |
| Poroikov V. | Drug repurposing for COVID-19 therapy: challenges and opportunities | 3.3 Chemoinformatics and chemical biology |
| Serebrennikova M. | Development of a searching algorithm for new cell-penetrating peptides based on machine learning methods | 3.3 Chemoinformatics and chemical biology |
| Sukhachev V. | Prediction of adverse effects of drug-drug interactions on the cardiovascular system based on the analysis of structure-activity relationships | 3.3 Chemoinformatics and chemical biology |
| Tabassum S. | Network Pharmacology and Molecular Docking Techniques to Elucidate the Mechanisms of Ocimum Sanctum against Tuberculosis | 3.3 Chemoinformatics and chemical biology |
| Tarasova O. | Computational Analysis of Viral Drug Resistance and Treatment Efficacy: Case study for HIV-infection | 3.3 Chemoinformatics and chemical biology |
| Zavestovskaya I. | Nanoteranostics based on multimodal nanoparticles and nanosystems | 3.3 Chemoinformatics and chemical biology |
| Bragina E. | Understanding the role of genetics in comorbidity | 4.1 Human medical/population genomics and genetics |
| Bushueva O. | Molecular mechanisms of cardio- and cerebrovascular comorbidity: from experimental analysis of structural and epigenetic variations in the human genome to post-GWAS analysis of genetic correlations between diseases | 4.1 Human medical/population genomics and genetics |
| Kharkov V. | Detailed phylogeny of the Y-chromosome haplogroup N1a2 in the populations of Siberia and Eastern Europe | 4.1 Human medical/population genomics and genetics |
| Ponomarenko M. | Bioinformatics search for genetic markers of socially significant diseases in promoters of human genes | 4.1 Human medical/population genomics and genetics |
| Savvina M. | DNA-microarray as an alternative diagnostic tool for targeted genetic carrier screening in population of Yakut ethnic group | 4.1 Human medical/population genomics and genetics |
| Shnaider T. | Investigation of the mechanisms of neurodevelopmental disorders caused by mutations in the CNTN6 gene on the cerebral organoids model | 4.1 Human medical/population genomics and genetics |
| Belonogova N. | Association of PANX3 with chronic back pain discovered using gene-based analysis | 4.2 Genome-wide association studies |
| Elgaeva E. | Bidirectional Mendelian randomization study reveals causal effects of psychosocial factors on chronic back pain and vice versa | 4.2 Genome-wide association studies |
| Loboda A. | A platform for case-control matching enables association studies without genotype sharing | 4.2 Genome-wide association studies |
| Nedoluzhko A. | Ancient DNA analysis of mummies from the post-Scythian Oglakhty cemetery in South Siberia. | 4.3 Human origin and evolution |
| Kovtun A. | Bioinformatics approaches and methods for analysis of gut microbiota | 5.1 Biotechnology through the prism of microbiome |
| Panikov N. | Genome-scale reconstructions of microbial biodynamics great expectations | 5.1 Biotechnology through the prism of microbiome |
| Sonets I. | Investigating bacterial and yeast diversity of spontaneous fermentation beer and cider using Hi-C metagenomics | 5.1 Biotechnology through the prism of microbiome |
| Zakhartsev M. | On the system-biological approach in modern C1-biotechnological research | 5.1 Biotechnology through the prism of microbiome |
| Afonnikova S. | Comparative and evolution genomics of somatic antigens of Oxalobacteraceae family | 5.2 Microbial communities of natural and anthropogenic habitats |
| Babich T.L. | Diversity and possible metabolic activity of the microbial community in nitrate- and radionuclide-contaminated groundwater | 5.2 Microbial communities of natural and anthropogenic habitats |
| Dedysh S. | Methanotroph diversity in natural and anthropogenic habitats as an inexhaustible source of novel biotechnologically relevant strains | 5.2 Microbial communities of natural and anthropogenic habitats |
| Ershov A. | Halophilic bacteria of the genera Halomonas and Marinobacter from petroleum reservoirs and their possible applications in biotechnology | 5.2 Microbial communities of natural and anthropogenic habitats |
| Litti Y. | Effect of organic loading rate on the biohythane production in the continuous two-stage anaerobic digestion of confectionery wastewater | 5.2 Microbial communities of natural and anthropogenic habitats |
| Mikhaylova Y. | Whole-genome analysis of Staphylococcus aureus isolates from ready-to-eat food | 5.2 Microbial communities of natural and anthropogenic habitats |
| Morozov A. | Elusive recurrent bacterial contamination in a diatom culture: a case study | 5.2 Microbial communities of natural and anthropogenic habitats |
| Kabardin I. | Development of optical and ultrasound methods for the multi-phase flows investigation and numerical calculations verification in biotechnological process | 5.3 Industrial biotechnologies: creation of producer strains |
| Moskalensky A. | A device reporting cell culture growth in CO2 incubator via Bluetooth | 5.3 Industrial biotechnologies: creation of producer strains |
| Sheremetieva M. | Metabolic engineering of corynebacteria to create a producer of L-valine | 5.3 Industrial biotechnologies: creation of producer strains |
| Stavrovskaya A. | Bioinformatics and experimental approaches in the creation of the drug “Superbact” for parkinsonism’s treatment | 5.3 Industrial biotechnologies: creation of producer strains |
| Korenskaia A. | Bioinformatic assessment of factors affecting the correlation between protein abundance and elongation efficiency in prokaryotes | 5.4 Modeling and computer analysis of microbiological systems and processes |
| Kazantsev F. | Finding the ways for potential increasing of L-valine biosynthesis yield in C. glutamicum with mathematical modeling | 5.4 Modeling and computer analysis of microbiological systems and processes |
| Gentzbittel L. | Whole genome-based breeding in legumes | 6.1 Genomics, genetics and system biology of plants |
| Kiseleva A. | Dissection of candidate genes for grain texture in common wheat | 6.1 Genomics, genetics and system biology of plants |
| Kozlova L. | Methods to investigate the biomechanics of the plant cell wall | 6.1 Genomics, genetics and system biology of plants |
| Nizhnikov A. | Amyloid formation is a widespread phenomenon in land plant species | 6.1 Genomics, genetics and system biology of plants |
| Novikova P. | Adaptation to polyploidy in Siberian Arabidopsis lyrata | 6.1 Genomics, genetics and system biology of plants |
| Shmakov N. | Plant immunity genes of Solanum tuberosum L cultivars associ-ated with resistance to potato late blight | 6.1 Genomics, genetics and system biology of plants |
| Kharchenko V. | The real function of the gene TERMINAL FLOWER 1 | 6.2 Developmental biology of plants: computational and experimental approaches |
| Kiryushkin A. | Does DEEPER ROOTING 1 (DRO1) gene of cucumber (Cucumis sativus L.) regulate a lateral root angle? | 6.2 Developmental biology of plants: computational and experimental approaches |
| Potsenovskaia E. | NF-Y genes in the somatic embryogenesis | 6.2 Developmental biology of plants: computational and experimental approaches |
| Sidorenko A. | Understanding the role of MAKR6 in Arabidopsis thaliana L. root development | 6.2 Developmental biology of plants: computational and experimental approaches |
| Tvorogova V. | Transcriptomic analysis for the optimization of transformation of legumes | 6.2 Developmental biology of plants: computational and experimental approaches |
| Xiuwei Cao | Condensation of STM empowers meristem activity and enhances salt tolerance | 6.2 Developmental biology of plants: computational and experimental approaches |
| Bashirzade A. | Combined treatment with autophagy inducers rapamycin and trehalose as an experimental therapy for Alzheimer’s disease-like pathology in mice | 7.1 Genomics, genetics and system biology of animals |
| Kalueff A. | Neurogenomics of zebrafish | 7.1 Genomics, genetics and system biology of animals |
| Korolenko T. | Neurodegeneration and diabetes; positive effect of trehalose in behavior of db/db mice, model of diabetes | 7.1 Genomics, genetics and system biology of animals |
| Mutovina A. | Different immune challenges alter hypothalamus microglia and astrocytes activity in the adult BTBR and C57Bl/6 mice. | 7.1 Genomics, genetics and system biology of animals |
| Nazarova G. | Hereditary predisposition to seizures in response to handling and its relation to life expectancy in the water vole (Arvicola amphibius L.) | 7.1 Genomics, genetics and system biology of animals |
| Romashchenko А. | Effect of divalent cations on the uptake of Mn3O4 nanoparticles by olfactory epithelial cells | 7.1 Genomics, genetics and system biology of animals |
| Salmina A. | Modern models of brain diseases for translational studies | 7.1 Genomics, genetics and system biology of animals |
| Savkin I. | Effects of the original synthetic ligand of benzodiazepine receptors in long-term alcoholized mice | 7.1 Genomics, genetics and system biology of animals |
| Sharapova M. | The role of manganese oxide nanoparticles in the formation of stress granules in the mouse olfactory system | 7.1 Genomics, genetics and system biology of animals |
| Smirnova K. | Effect of chronic unpredictable mild stress on stress resilience in Disc1-Q31L mice | 7.1 Genomics, genetics and system biology of animals |
| Stanova A. | The impact of temperature conditions of incubation on mouse embryonic development during in vitro fertilization | 7.1 Genomics, genetics and system biology of animals |
| Milyaeva P. | The role of the rhino gene in the transcriptional regulation of different piRNA clusters | 7.2 Population and evolutionary genomics/genetics of wild and domestic animals |
| Trapezov O. | Regulatory effects of genes controlling behavior and painting shaping control. American mink (neovison vison) as a model | 7.2 Population and evolutionary genomics/genetics of wild and domestic animals |
| Yudin N. | How does a harsh environment make wild and domestic animals evolve similarly? | 7.2 Population and evolutionary genomics/genetics of wild and domestic animals |
| Bezdvornykh I. | Swaveform: a genome-wide survey of structural variation profiles | 8. Biomedicine, bioinformatics and systems computational biology |
| Bratus A. | Mathematical model dynamics of pancreatic cancer with interaction of immune system. Approach by replicator quasispecies system. | 8. Biomedicine, bioinformatics and systems computational biology |
| Chemeris D. | New solutions for medical genetics and biotechnologies from Maxim Medikal LLC | 8. Biomedicine, bioinformatics and systems computational biology |
| Davydova A. | Hemoglobin-binding 2′-F-modified RNA aptamers: structure studies and analytical application | 8. Biomedicine, bioinformatics and systems computational biology |
| Fishman V. | Applications of next-generation sequencing for clinical diagnostics of monogenic diseases | 8. Biomedicine, bioinformatics and systems computational biology |
| Korolenko T. | Autophagy inducer trehalose positive effect in db/db mice, genetic model of diabetes | 8. Biomedicine, bioinformatics and systems computational biology |
| Kovner A. | A new approach to stimulating the wound healing based on Opisthorchis felineus proteins | 8. Biomedicine, bioinformatics and systems computational biology |
| Kozhevnikova O. | Pharmacogenetic influence of complement genes genotypes on the response to anti-VEGF treatment for age-related macular degeneration in a Russian population | 8. Biomedicine, bioinformatics and systems computational biology |
| Krasner K. | Relex Smile lenticules as a source of obtaining stromal corneal cells | 8. Biomedicine, bioinformatics and systems computational biology |
| Letyagin A. | Artificial intelligence (AI) of 3D MRI images for neurooncology | 8. Biomedicine, bioinformatics and systems computational biology |
| Nebrat V. | Human water model: interstitium and meridians of traditional Chinese medicine | 8. Biomedicine, bioinformatics and systems computational biology |
| Permyakova E. | Curdlan/Chitosan/Ag NPs Foams for healing chronic wounds | 8. Biomedicine, bioinformatics and systems computational biology |
| Popov D. | Role of HSP70 proteins in regulation of mitochondrial protein content in skeletal muscle | 8. Biomedicine, bioinformatics and systems computational biology |
| Saik O. | Uncovering the genes linking glucose variability with endothelial dysfunction in diabetes by the analysis of gene networks | 8. Biomedicine, bioinformatics and systems computational biology |
| Shatunova E. | Dkk-1 level detection in ankylosing spondylitis by the aptamer-based colorimetric assay | 8. Biomedicine, bioinformatics and systems computational biology |
| Shelenkov A. | Computational pipeline and database for the epidemiological typing and antimicrobial resistance profiling of important bacterial pathogens | 8. Biomedicine, bioinformatics and systems computational biology |
| Sitnikova N. | Ag modification of PLC-COOH nanofibers provides mesenchymal cells proliferation | 8. Biomedicine, bioinformatics and systems computational biology |
| Turubanova V. | Vaccination with dendritic cells pulsed with glioma killed by photodynamic therapy induces efficient antitumor immunity | 8. Biomedicine, bioinformatics and systems computational biology |
| Vlasenkova R. | Construction of comprehensive amino acid networks from Molecular Dynamics trajectories and tumor mutational profile of human sodium transporter NaPi2b | 8. Biomedicine, bioinformatics and systems computational biology |
| Vorobyeva M. | Oligonucleotide aptamers for diagnosis and treatment of human diseases | 8. Biomedicine, bioinformatics and systems computational biology |
| Arefieva N. | Detection of recombination events in coronavirus genomes from the subgenus sarbecovirus | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Basu A. | Epigenetics behind SARS COV2 | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Bocharov G. | Mathematical modelling of antiviral immune responses to SARS-CoV-2 infection | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Das R. | Genomic variation underlies the severity of COVID-19 clinical manifestation | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Djordjevic M. | Combining machine learning and non-linear dynamics modeling to understand COVID-19 risk factors | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Efimov K. | New features of the PCA-Seq method in time series analysis (on the example of COVID-19) | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Gorelov S. | Finding possible inhibitors for SARS-CoV-2 proteins using virtual ligand screening | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Ilic B. | Analytical and numerical study of infection progression under social distancing measures | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Kaminskiy G. | Behavior changes of epidemic control in condition of sufficient and limited resources | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Kisselevskaya-Babinina V. | Markov model of hospitalized patients with COVID-19 | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Kolokoltsov V. | Fighting ticks with functional-analytic guns | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Kornienko E. | The potential usability of Oxford Nanopore Technology for revealing of SARS-CoV-2 dual infection and intra-host viral variability in clinical samples | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Krivorotko O. | Mathematical modeling and identifiability aspects in epidemiology | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Miroshnichenko M. | Mathematical modeling of SARS-CoV-2 infection process and virus spreading in the human body considering B and T cell-mediated immune responses | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Nenad Mitic | Large scale clustering in structural and evolutionary analysis of SARS-CoV-2 proteins | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Netesov S. | Coronavirus SARS-CoV2 preliminary results of the pandemic | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Novikov K. | Coupled agent-based model of COVID-19 and major depressive disorder spread in Moscow | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Petrakova V. | Mathematical modeling of the dynamics of incomes of the population under epidemic process | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Rodic A. | Modeling the COVID-19 transmission dynamics and identifying meteorological and sociodemographic predictors | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Sannikova T. | Seeking the threshold for herd immunity in the synthetic population of a medium-sized city | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Taranik A. | Forecast of the development of the COVID-19 epidemic situation in Moscow in 2022-2023 | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Trusov N. | Consumer loan demand modeling of households in Russia under sanctions | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Vlad A. | Transmission of acute respiratory infections in a city: agent-based approach | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Zvonareva T. | Mathematical modeling of information diffusion in Twitter | 9. Mathematics, bioinformatics and systems computational biology of COVID-19 |
| Eremin D. | Effects of central administration of Cerebral dopamine neurotrophic factor (CDNF) on the behavior and serotonin system in the mouse brain | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Kaminskaya Ya. | HippocampalВ overexpression of cerebral dopamine neurotrophic factor (CDNF)В affected the behavior of mice with genetically-defined depressive-like behavior | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Kazantseva A. | The use of genetic risk score approach to predict higher depression risk in young adults | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Klemeshova D. | Analysis of behavioral and EEG responses given the performing tasks to control attention and behavior in children with emotional instability | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Ozgoren M. | Brain functioning across regions and dimensions lessons learned from dic | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Ratushnyak A. | The emergence and evolution of living systems compensating for entropy processes | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Shtern E. | P.Y. Halperin’s Activity Theory and Language Consciousness Theory: Results of EEG-Based Research in the Perspective of ICT-assisted Foreign Language Learning | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Smirnova L. | IgG-Dependent dismutation of Superoxide and DPPH radical in patients with schizophrenia | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Valdes-Sosa | Probing developmental disorders with multivariate quantitative EEG analysis | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Chernigovskaya T. | Homo semiotocus: Neuroscience and Other Logics | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Anohin K. | Природа когнитивной информации | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Kireev M. | Мозговые механизмы обработки многозначной информации | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Smirnova L. | IgG-Dependent dismutation of Superoxide and DPPH radical in patients with schizophrenia | 10. Cognitive sciences, neurogenetics, neuroinformatics and systems computational biology |
| Alemasova E. | Role of PAR and RNA in biomolecular condensate formation | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Bogachev S. | The novel radioprotective agent | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Diatlova E. | Base excision repair in non-canonical DNA structures | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Dyrkheeva N. | TOP1, TDP1, and PARP1 inhibition: coupling and association | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Endutkin A. | Bioorthogonality in the light of cell defense mechanisms: interaction of non-natural nucleic acids with the base excision dna repair system | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Kubareva E. | G-quadruplex formed by the promoter region of the hTERT gene:
structure-driven effects on DNA mismatch repair functions | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Kulms D. | Focal adhesion kinase plays a dual role in TRAIL resistance and metastatic outgrowth of malignant melanoma | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Kutuzov M. | PARP2 and BER in the nucleosomal context | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Lavrik O. | Regulation of PARP1 activity by its protein partners | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Maluchenko N. | Nucleosome reorganization by PARP-1 | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Zharkov D. | Structure and function evolution in DNA repair | 11. Systems biology and bioinformatics of DNA repair processes and programmed cell death |
| Alekseev A. | Validation of metabolic aging trends on multiple databases | 12. Systems biology of aging: experimental and computational approaches |
| Anisimov V. | Multi-stage model of aging and carcinogenesis | 12. Systems biology of aging: experimental and computational approaches |
| Evgen’ev M. | Hsp70 and donors of H2S in aging-associated diseases and inflammation | 12. Systems biology of aging: experimental and computational approaches |
| Khalyavkin A. | Aging is a sequential multisystem syndrome, preventable and reversible under certain conditions | 12. Systems biology of aging: experimental and computational approaches |
| Khokhlov A. | Fighting Alzheimer’s disease: is there a chance to conquer? | 12. Systems biology of aging: experimental and computational approaches |
| Kolodkin A. | ROS networks: designs, aging, Parkinson’s disease and preci-sion therapies | 12. Systems biology of aging: experimental and computational approaches |
| Kriukov D. | Longevity And Rejuvenation Effects Of Cell Reprogramming Are Decoupled From The Loss Of Somatic Identity | 12. Systems biology of aging: experimental and computational approaches |
| Morgunova G. | Relationship between basal metabolic rate, muscle strength and aging | 12. Systems biology of aging: experimental and computational approaches |
| Rudnitskaya E. | Alteration of hippocampal glial support during early postnatal development as a possible premise of Alzheimer’s disease | 12. Systems biology of aging: experimental and computational approaches |
| Salmina A. | Aberrant neuroplasticity in aging and neurodegeneration associated with deregulation of neurogenesis and angiogenesis | 12. Systems biology of aging: experimental and computational approaches |
| Rybina O. | Transcription regulation in aging and lifespan control | 12. Systems biology of aging: experimental and computational approaches |
| Antezana M. | A novel, computationally tractable algorithm flags in big matrices every column associated in any way with others or a dependent variable, with much higher power when columns are linked like mutations in chromosomes | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Brodt K. | Deep learning for the development of an OCR for old Tibetan books | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Gam A. | Implementation of a spanning trees sampling algorithm to detect 4-motifs | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Genaev M. | Classification of fruit flies by gender in images based on the YOLOv4-tiny neural network using mobile devices | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Golubyatnikov V. | Mathematical and numerical modelling of the circadian oscillator | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Golubyatnikov V. | On uniqueness and stability of a cycle of one Elowitz-Leibler type dynamical system | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Karpenko D. | Multidimensional analysis, dimension reduction, categorization with statistical approach – stability and reproducibility | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Karpenko D. | Recursive matrix algorithm for calculating differential expressions | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Lakhno V. | Bioinformatics and nanobioelectronics. Informatics based on biocomputer technologies | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Meshcheryakov G. | Beta negative binomial mixture model facilitates identification of allele-specific gene regulation in high-throughput sequencing data | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Palyanov A. | Towards artificial minds through computer simulation of natural ones, from simple to more complex | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Titov A. | Space-time correlation analysis in bio-macro-molecules, based on molecular dynamic trajectory data | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Palyanov A. | Towards artificial minds through computer simulation of natural ones, from simple to more complex | 13. Big genetic Data Analysis, deep learning, mathematical modeling and supercomputing |
| Danilenko V. | Study of human and animal microbiome as genetic and pharmacological resources for the development of innovative biotechnologies for medicine, animal husbandry and agro-industrial complex. | Plenary session |
| Kulakovskiy I. | Interpreting non-coding genome variation with DNA sequence motifs | Plenary session |
| Sulgina T. | 3D-Tomography without Tomographic Equipment. Bioluminescence and Fluorescence In Vivo Imaging System Newton 7.0 (Vilber) | Plenary session |
| Popov V. | From 3D protein structure to biological function
| Plenary session |