Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
Moscow, Moscow, Russian Federation
Engelhardt Institute of Molecular Biology of RAS
Moscow, Moscow, Russian Federation
from 01.01.2019 until now
Institute of Cytology and Genetics SB RAS
Novosibirsk State University
Peoples’ Friendship University of Russia (Agrarno-Tehnologicheskiy Institut, professor)
from 01.01.2020 to 01.01.1921
Moscow, Moscow, Russian Federation
Computer reconstruction of gene networks—sets of interacting macromolecules with common functions – is a complex task in bioinformatics. Gene networks, presented as graphical structures, offer a convenient means of visualization and provide qualitatively new insights into the set of studied genes and their biological functions. Expanding further, we explore associative gene networks related to diseases. These networks encompass not only genes and their products (proteins, metabolites, non-coding RNA and drug compounds) but also common disease indicators (symptoms, phenotypic manifestations). Software tools aimed at reconstructing gene network graphs are undergoing active development worldwide and find significant utility in biomedicine. We evaluate available online bioinformatics tools for gene network reconstruction based on gene lists and corresponding computer analysis pipelines. We delve into examples showcasing the utilization of online programs for analyzing the glioma gene network. The proposed approach can be extended to other functional gene sets linked to diseases. Scrutinizing the structure of disease-associated gene networks enables the identification of pivotal genes, which can serve as therapeutic targets. Employing similar bioinformatics strategies, gene networks of intricate disorders such as Parkinson's disease, dementia, schizophrenia, breast cancer, and other cancers have been explored. We delve into the applications of computer programs for gene network analysis and address pedagogical aspects of the field.
bioinformatics, gene networks, glioma, complex diseases, protein-protein interactions, medical informatics
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