Bioinformatics of genome regulation and structure – 2020 papers collection

Orlov, Yuriy L.; Dobrovolskaya, O. B.; Chen, Ming; Hofestädt, Ralf

doi:10.1515/jib-2020-0038

Journal of Integrative Bioinformatics

2020

DOI: 10.1515/jib-2020-0038

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Bioinformatics of genome regulation and structure – 2020 papers collection

Yuriy L. Orlov

O. B. Dobrovolskaya

Ming Chen

et al.

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Cited by 3 publications

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“…Gene expression regulation is one of the priority topics for bioinformatics development. The topic was one of background research areas for the Journal of Integrative Bioinformatics [ 1 , 2 ]. Experimental studies of gene regulation at transcription level led to the development of the databases on transcription factor regulation – TRRD and TRANSFAC [ 3 ].…”

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confidence: 99%

“…Recent BGRS\SB meeting was hold in July 2022 ( https://bgrssb.icgbio.ru/2022/ ). Previously we had special collection of the papers from the conference materials discussed at BGRS-2020 [ 2 ] and earlier BGRS events [ 13 – 15 ].…”

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confidence: 99%

“…We had organized publications following the BGRS conferences in special journal issues at BioMed Central – BMC Genomics [ 13 , 36 ], BMC Genetics [ 26 ], BMC Medical Genomics [ 27 , 37 ], and Journal of Bioinformatics and Computational Biology [ 14 , 15 , 38 ] as well. The Journal of Integrative Bioinformatics supported this field by organization of special journal issues and promoting international collaboration on bioinformatics [ 2 ]. Thus, research on gene expression topic is in high demand.…”

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BGRS: bioinformatics of genome regulation and data integration

Orlov,

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et al. 2023

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confidence: 99%

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confidence: 99%

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BGRS: bioinformatics of genome regulation and data integration

Orlov,

Chen,

Kolchanov

et al. 2023

Journal of Integrative Bioinformatics

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Web-Services for Microrna Target Prediction Using Neural Networks

Aristarkhov,

Dergilev,

Potapova

et al. 2024

Russian Journal of Biological Physics and Chemisrty

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The pursuit of microRNA target genes necessitates the creation of novel software and web services. MicroRNAs, abbreviated as short non-coding RNA molecules, hold a pivotal role in metabolic regulation, plant responses to environmental stress, and gene expression. Gaining insights into microRNA functions and investigating their target genes can advance drug development and address biotechnological challenges. However, the study and identification of microRNA targets within the genome present technical obstacles. MicroRNA molecules may not exhibit complete complementarity with their mRNA targets. These molecules either contribute to mRNA degradation or inhibit translation, and this process can transpire without full target complementarity. Consequently, the delineation of targets solely based on the principle of complementarity lacks unequivocal clarity. Moreover, a single microRNA molecule can correspond to multiple target genes simultaneously. The solution entails harnessing substantial datasets, employing machine learning techniques, and leveraging neural networks. In bioinformatics, neural networks serve a variety of functions, encompassing the analysis of biomedical data, diagnostics, prediction, classification, and nucleotide sequence segmentation. The pursuit and anticipation of microRNA targets through machine learning methods are currently undergoing vigorous development. A comparative assessment of contemporary neural networks for this task has been executed. A neural network-driven web service for microRNA prediction has been created. The server aspect of the service was developed using the Python programming language and the Flask library. The Mitar neural network, founded on deep learning, was employed. This network demonstrates heightened precision in predicting microRNA targets. We deliberate on the applications of miRNA prediction in gene expression analysis. Sustained research efforts are imperative to enhance the efficiency and broaden the capabilities of the developed computer system.

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Reconstruction of Gene and Associative Networks of Diseases to Search for Target Genes

Turkina,

Iarema,

Mayorova

et al. 2024

Russian Journal of Biological Physics and Chemisrty

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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.

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Bioinformatics of genome regulation and structure – 2020 papers collection

Cited by 3 publications

References 17 publications

BGRS: bioinformatics of genome regulation and data integration

BGRS: bioinformatics of genome regulation and data integration

Web-Services for Microrna Target Prediction Using Neural Networks

Reconstruction of Gene and Associative Networks of Diseases to Search for Target Genes

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