Detecting key genes relative expression orderings as biomarkers for machine learning-based intelligent screening and analysis of type 2 diabetes mellitus

Xie, Xueqin; Wu, Changchun; Ma, Caiyi; Gao, Dong; Su, Wei; Huang, Jian; Deng, Kejun; Yan, Dan; Lin, Hao

doi:10.1016/j.eswa.2024.124702

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.124702

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Detecting key genes relative expression orderings as biomarkers for machine learning-based intelligent screening and analysis of type 2 diabetes mellitus

Xueqin Xie,

Changchun Wu,

Caiyi Ma

et al.

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Cited by 2 publications

References 60 publications

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Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi‐omics data and machine learning analysis

Yong,

Hu,

Kang

et al. 2024

IET Systems Biology

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Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi‐omics data analysis and machine learning techniques. Single‐cell RNA sequencing disclosed a heightened presence of macrophages and CD8‐positive alpha‐beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell–cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.

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Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi‐omics data and machine learning analysis

Yong,

Hu,

Kang

et al. 2024

IET Systems Biology

View full text Add to dashboard Cite

show abstract

Inference and analysis of cell‐cell communication of non‐myeloid circulating cells in late sepsis based on single‐cell RNA‐seq

Tao,

Li,

Liu

2024

IET Systems Biology

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Sepsis is a severe systemic inflammatory syndrome triggered by infection and is a leading cause of morbidity and mortality in intensive care units (ICUs). Immune dysfunction is a hallmark of sepsis. In this study, the authors investigated cell‐cell communication among lymphoid‐derived leucocytes using single‐cell RNA sequencing (scRNA‐seq) to gain a deeper understanding of the underlying mechanisms in late‐stage sepsis. The authors’ findings revealed that both the number and strength of cellular interactions were elevated in septic patients compared to healthy individuals, with several pathways showing significant alterations, particularly in conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs). Notably, pathways such as CD6‐ALCAM were more activated in sepsis, potentially due to T cell suppression. This study offers new insights into the mechanisms of immunosuppression and provides potential avenues for clinical intervention in sepsis.

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Detecting key genes relative expression orderings as biomarkers for machine learning-based intelligent screening and analysis of type 2 diabetes mellitus

Cited by 2 publications

References 60 publications

Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi‐omics data and machine learning analysis

Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi‐omics data and machine learning analysis

Inference and analysis of cell‐cell communication of non‐myeloid circulating cells in late sepsis based on single‐cell RNA‐seq

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