Introduction

Nesterova, Anastasia P.; Klimov, Eugene; Zharkova, Maria; Sozin, Sergey; Соболев, В. И.; Ivanikova, Natalia V.; Shkrob, Maria; Yuryev, Anton

doi:10.1016/b978-0-12-817086-1.00001-4

Cited by 8 publications

(14 citation statements)

References 72 publications

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“…For filling disease templates with sentences and citations about characteristics of diseases (molecular, clinical, pharmacological, etc. ), we used ETM (https://demo.elseviertextmining.com), which was built around MedScan technology (Nesterova et al, 2020; Novichkova et al, 2003). MedScan text mining has high confidence scores (recall and precision) for annotating biomedical facts and retrieving quates from the literature (F1 scores of 90.6 for disease concepts, 87.4 for anatomy concepts, 91.1 in general for combined molecular, medical, pharmacological concepts) (not published internal evaluation).…”

Section: Resultsmentioning

confidence: 99%

“…Etiology/Pathology section includes lists of mutated genes, known biomarkers and cell signaling (pathways) which may describe molecular causes of the disease. We used commercial software Pathways Studio (https://www.pathwaystudio.com/) and biomedical network Resnet – 2020 (Nesterova et al, 2020) which contains associations and links between proteins, genes, diseases and other concepts extracted from the literature based on the descried above text mining technology (Figure 4). Higher connectivity number in the network (Table 1) indicates that the disease has more known associated proteins, cell processes, drugs, clinical parameters, or anatomy concepts.…”

Section: Resultsmentioning

confidence: 99%

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Monogenetic rare diseases in biomedical databases and text mining

Nesterova

Klimov

Sozin

et al. 2022

Preprint

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The testing of pharmacological hypotheses becomes faster and more accurate, but at the same time more difficult than even two decades ago. It takes more time to collect and analyse disease mechanisms and experimental facts in various specialized resources. We discuss a new approach to aggregating individual pieces of information about a single disease using Elsevier's automated text mining technology. The developed algorithm allows for the collection of published facts in a unified format starting only with the name of the disease. A special template, which combines research and clinical descriptions of diseases was developed. The approach was tested, and information was collected for 55 rare monogenic diseases. Clinical, molecular, and pharmacological characteristics of diseases with supporting references from the literature are available in the form of tables and files. Manually curated templates for 10 rare diseases, including top ranked Cystic Fibrosis and Huntington's disease were published to demonstrate the results of the described approach.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Monogenetic rare diseases in biomedical databases and text mining

Nesterova

Klimov

Sozin

et al. 2022

Preprint

Self Cite

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“…Results of enrichment analysis highly depend on the input collections of pathways or protein groups (Nesterova, et al, 2020). For example, analysis using Gene Ontology, the most popular collection for enrichment algorithms, returned very broad and ambiguous cell processes, such as “regulation of cell proliferation” and “DNA-binding” (Supplementary file “Pathways enriched with activity regulators”).…”

Section: Resultsmentioning

confidence: 99%

A disease model for Diffuse Intrinsic Pontine Glioma (DIPG) with mutations in TP53 and its application for drug repurposing

Yuryev

Nesterova

Sozin

et al. 2022

Preprint

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Brain cancers are ones of most aggressive and difficult to treat cancers. Despite numerous studies of the cellular mechanisms of gliomas, it is difficult to stop tumor growth. A complex genetic and epigenetic nature of many gliomas and poorly known pathways of human neuron precursors maturation suggest turning to big data analysis to find new insights and directions for drug development. We developed in silico molecular models and predicted molecular switches in signaling cascades that maintain multipotency of neuronal precursor cells in diffuse intrinsic pontine glioma (DIPG) driven by the H3K27M mutation and mutations in the TP53 gene. Oncogenes and biomarkers were predicted based on transcriptomics and mutational genomics data from a cohort of 30 patients with DIPG analyzed using Elsevier artificial intelligence methods and a collection of manually curated cancer hallmark pathways. The molecular models of DIPG with mutations in TP53 and histone 3 gene describe the mechanism of oligodendrocyte dedifferentiation due to activation of transcriptional factors OLIG2, SOX2, and POU5F1, epithelial-to-mesenchymal transition via strong EGFR and TGFR signaling, enhanced cell response to hypoxia via HIF1A signaling and enhanced angiogenesis by VEGFA overexpression. Using in silico analysis, we identified drugs capable of inhibiting mutant TP53: vorinostat, cisplatin, paclitaxel, and statins were top-ranked drugs. The predicted drugs and oncogenes had individual patient-level differences that can be visualized with created DIPG model and may be useful for future research in the field of personalized medicine.

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“…We used the Pathway Studio® 9 desktop software with the ResNet®14 database and web version of the Pathway Studio software (https://mammalcedfx.pathwaystudio.com) (Elsevier). Signal pathways were created according to the algorithms we developed [170]. The results are presented in Figure 5 and are described below.…”

Section: Enrichment Analysismentioning

confidence: 99%

Role of PPARs in Progression of Anxiety: Literature Analysis and Signaling Pathways Reconstruction

et al. 2020

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Peroxisome proliferator-activated receptor (PPAR) group includes three isoforms encoded by PPARG, PPARA, and PPARD genes. High concentrations of PPARs are found in parts of the brain linked to anxiety development, including hippocampus and amygdala. Among three PPAR isoforms, PPARG demonstrates the highest expression in CNS, where it can be found in neurons, astrocytes, and glial cells. Herein, the highest PPARG expression occurs in amygdala. However, little is known considering possible connections between PPARs and anxiety behavior. We reviewed possible connections between PPARs and anxiety. We used the Pathway Studio software (Elsevier). Signal pathways were created according to previously developed algorithms. SNEA was performed in Pathway Studio. Current study revealed 14 PPAR-regulated proteins linked to anxiety. Possible mechanism of PPAR involvement in neuroinflammation protection is proposed. Signal pathway reconstruction and reviewing aimed to reveal possible connection between PPARG and CCK-ergic system was conducted. Said analysis revealed that PPARG-dependent regulation of MME and ACE peptidase expression may affect levels of nonhydrolysed, i.e., active CCK-4. Impairments in PPARG regulation and following MME and ACE peptidase expression impairments in amygdala may be the possible mechanism leading to pathological anxiety development, with brain CCK-4 accumulation being a key link. Literature data analysis and signal pathway reconstruction and reviewing revealed two possible mechanisms of peroxisome proliferator-activated receptors involvement in pathological anxiety: (1) cytokine expression and neuroinflammation mechanism and (2) regulation of peptidases targeted to anxiety-associated neuropeptides, primarily CCK-4, mechanism.

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Introduction

Cited by 8 publications

References 72 publications

Monogenetic rare diseases in biomedical databases and text mining

Monogenetic rare diseases in biomedical databases and text mining

A disease model for Diffuse Intrinsic Pontine Glioma (DIPG) with mutations in TP53 and its application for drug repurposing

Role of PPARs in Progression of Anxiety: Literature Analysis and Signaling Pathways Reconstruction

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