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  1. AU="Kavatagimath, Satish"
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  1. Article ; Online: Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity.

    Ganekal, Prashanth / Vastrad, Basavaraj / Kavatagimath, Satish / Vastrad, Chanabasayya / Kotrashetti, Shivakumar

    Medicina (Kaunas, Lithuania)

    2023  Volume 59, Issue 2

    Abstract: Background and Objectives: ...

    Abstract Background and Objectives:
    MeSH term(s) Humans ; MicroRNAs/genetics ; Protein Interaction Maps/genetics ; Gene Regulatory Networks ; Biomarkers ; Diabetes Mellitus ; Computational Biology ; Gene Expression Profiling
    Chemical Substances MicroRNAs ; Biomarkers
    Language English
    Publishing date 2023-02-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2188113-3
    ISSN 1648-9144 ; 1010-660X
    ISSN (online) 1648-9144
    ISSN 1010-660X
    DOI 10.3390/medicina59020309
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes Mellitus.

    Alur, Varun / Raju, Varshita / Vastrad, Basavaraj / Vastrad, Chanabasayya / Kavatagimath, Satish / Kotturshetti, Shivakumar

    Clinical medicine insights. Endocrinology and diabetes

    2023  Volume 16, Page(s) 11795514231155635

    Abstract: Background: Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM.: Methods: The next generation sequencing (NGS) dataset GSE81608 was retrieved ... ...

    Abstract Background: Type 2 diabetes mellitus (T2DM) is the most common metabolic disorder. The aim of the present investigation was to identify gene signature specific to T2DM.
    Methods: The next generation sequencing (NGS) dataset GSE81608 was retrieved from the gene expression omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs) between T2DM and normal controls. Then, Gene Ontology (GO) and pathway enrichment analysis, protein-protein interaction (PPI) network, modules, miRNA (micro RNA)-hub gene regulatory network construction and TF (transcription factor)-hub gene regulatory network construction, and topological analysis were performed. Receiver operating characteristic curve (ROC) analysis was also performed to verify the prognostic value of hub genes.
    Results: A total of 927 DEGs (461 were up regulated and 466 down regulated genes) were identified in T2DM. GO and REACTOME results showed that DEGs mainly enriched in protein metabolic process, establishment of localization, metabolism of proteins, and metabolism. The top centrality hub genes
    Conclusion: The potential crucial genes, especially
    Language English
    Publishing date 2023-02-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2628990-8
    ISSN 1179-5514
    ISSN 1179-5514
    DOI 10.1177/11795514231155635
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis.

    Pujar, Madhu / Vastrad, Basavaraj / Kavatagimath, Satish / Vastrad, Chanabasayya / Kotturshetti, Shivakumar

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 9157

    Abstract: Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In ... ...

    Abstract Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.
    MeSH term(s) Biomarkers ; Computational Biology/methods ; Diabetes Mellitus, Type 1/genetics ; Gene Ontology ; Humans ; MicroRNAs/genetics ; MicroRNAs/metabolism
    Chemical Substances Biomarkers ; MicroRNAs
    Language English
    Publishing date 2022-06-01
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-13291-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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