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  1. Article ; Online: Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine.

    Sinnarasan, Vigneshwar Suriya Prakash / Paul, Dahrii / Das, Rajesh / Venkatesan, Amouda

    Omics : a journal of integrative biology

    2023  Volume 27, Issue 6, Page(s) 260–272

    Abstract: Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) ... ...

    Abstract Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that
    MeSH term(s) Humans ; Biomarkers, Tumor/genetics ; Gene Regulatory Networks ; Precision Medicine ; Stomach Neoplasms/diagnosis ; Stomach Neoplasms/genetics ; Computational Biology/methods ; Machine Learning ; ATPases Associated with Diverse Cellular Activities/genetics ; Cell Cycle Proteins/genetics
    Chemical Substances Biomarkers, Tumor ; TRIP13 protein, human (EC 3.6.4.-) ; ATPases Associated with Diverse Cellular Activities (EC 3.6.4.-) ; Cell Cycle Proteins
    Language English
    Publishing date 2023-05-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2030312-9
    ISSN 1557-8100 ; 1536-2310
    ISSN (online) 1557-8100
    ISSN 1536-2310
    DOI 10.1089/omi.2023.0015
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection.

    Das, Rajesh / Sinnarasan, Vigneshwar Suriya Prakash / Paul, Dahrii / Venkatesan, Amouda

    Biochemical genetics

    2023  Volume 62, Issue 2, Page(s) 987–1006

    Abstract: Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to ... ...

    Abstract Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.
    MeSH term(s) Humans ; Gene Regulatory Networks ; COVID-19/genetics ; SARS-CoV-2/genetics ; SARS-CoV-2/metabolism ; MicroRNAs/genetics ; MicroRNAs/metabolism ; Machine Learning
    Chemical Substances MicroRNAs
    Language English
    Publishing date 2023-07-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2168-4
    ISSN 1573-4927 ; 0006-2928
    ISSN (online) 1573-4927
    ISSN 0006-2928
    DOI 10.1007/s10528-023-10458-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 578: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Correction: A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection.

    Das, Rajesh / Sinnarasan, Vigneshwar Suriya Prakash / Paul, Dahrii / Venkatesan, Amouda

    Biochemical genetics

    2023  Volume 62, Issue 2, Page(s) 1007

    Language English
    Publishing date 2023-09-05
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 2168-4
    ISSN 1573-4927 ; 0006-2928
    ISSN (online) 1573-4927
    ISSN 0006-2928
    DOI 10.1007/s10528-023-10511-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 578: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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