AIMC Topic: Transcriptome

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Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.

Scientific reports
Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathophysiologically unrelated, AD is a common skin disease characterized by chronic inflammation and skin barrier dysfunction, whereas T2DM is a metabolic ...

Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.

Journal of computational biology : a journal of computational molecular cell biology
With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable ...

Identification and verification of the optimal feature genes of ferroptosis in thyroid-associated orbitopathy.

Frontiers in immunology
BACKGROUND: Thyroid-associated orbitopathy (TAO) is an autoimmune inflammatory disorder of the orbital adipose tissue, primarily causing oxidative stress injury and tissue remodeling in the orbital connective tissue. Ferroptosis is a form of programm...

The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model.

Frontiers in immunology
BACKGROUND: Sepsis is a life-threatening organ dysfunction condition produced by dysregulation of the host response to infection. It is now characterized by a high clinical morbidity and mortality rate, endangering patients' lives and health. The pur...

Cross-disease transcriptomic analysis reveals DOK3 and PAPOLA as therapeutic targets for neuroinflammatory and tumorigenic processes.

Frontiers in immunology
OBJECTIVE: Subarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles and underlying mechanisms remain poorly understood. This study aims to identify differentially expr...

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.

Breast cancer research and treatment
BACKGROUND: This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses.

Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks With Adaptively Feature-Spatial Balance and Contrastive Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information ha...

Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.

Genomics
BACKGROUND: Abnormal programmed cell death (PCD) plays a central role in spermatogenic dysfunction. However, the molecular mechanisms and biomarkers of PCD in patients with nonobstructive azoospermia (NOA) remain unclear.

Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.

Scientific reports
BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aimed to probe inflammation pathways at the genetic level and pinpoint biomarkers linked to HCC patient ...