AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Cellular Senescence

Showing 1 to 10 of 30 articles

Clear Filters

Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing.

PloS one
BACKGROUND: The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft ...

Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

Frontiers in immunology
BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outc...

An object detection-based model for automated screening of stem-cells senescence during drug screening.

Neural networks : the official journal of the International Neural Network Society
Deep learning-based cell senescence detection is crucial for accurate quantitative analysis of senescence assessment. However, senescent cells are small in size and have little differences in appearance and shape in different states, which leads to i...

Identification of diagnostic genes and the miRNA‒mRNA‒TF regulatory network in human oocyte aging via machine learning methods.

Journal of assisted reproduction and genetics
PURPOSE: Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics meth...

Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.

Nature aging
Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and...

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

International journal of molecular sciences
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune mic...

SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden.

Genome medicine
BACKGROUND: Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets h...

The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis.

BMC psychiatry
BACKGROUND: Evidence indicates that patients with Major Depressive Disorder (MDD) exhibit a senescence phenotype or an increased susceptibility to premature senescence. However, the relationship between senescence-related genes (SRGs) and MDD remains...