AIMC Topic: Transcriptome
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Machine learning analysis of gene expression profiles of pyroptosis-related differentially expressed genes in ischemic stroke revealed potential targets for drug repurposing.
Scientific reports
Feb 27, 2025
The relationship between ischemic stroke (IS) and pyroptosis centers on the inflammatory response elicited by cerebral tissue damage during an ischemic stroke event. However, an in-depth mechanistic understanding of their connection remains limited. ...
Multi-omics analyses and machine learning prediction of oviductal responses in the presence of gametes and embryos.
eLife
Feb 26, 2025
The oviduct is the site of fertilization and preimplantation embryo development in mammals. Evidence suggests that gametes alter oviductal gene expression. To delineate the adaptive interactions between the oviduct and gamete/embryo, we performed a m...
Leveraging diverse cell-death patterns in diagnosis of sepsis by integrating bioinformatics and machine learning.
PeerJ
Feb 26, 2025
BACKGROUND: Sepsis is a life-threatening disease causing millions of deaths every year. It has been reported that programmed cell death (PCD) plays a critical role in the development and progression of sepsis, which has the potential to be a diagnosi...
Multiscale Dissection of Spatial Heterogeneity by Integrating Multi-Slice Spatial and Single-Cell Transcriptomics.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 25, 2025
The spatial structure of cells is highly organized at multiscale levels from global spatial domains to local cell type heterogeneity. Existing methods for analyzing spatially resolved transcriptomics (SRT) are separately designed for either domain al...
Tisslet tissues-based learning estimation for transcriptomics.
BMC bioinformatics
Feb 25, 2025
In the context of multi-omics data analytics for various diseases, transcriptome-wide association studies leveraging genetically predicted gene expression hold promise for identifying novel regions linked to complex traits. However, existing methods ...
Integrated multiomics analysis and machine learning refine neutrophil extracellular trap-related molecular subtypes and prognostic models for acute myeloid leukemia.
Frontiers in immunology
Feb 24, 2025
BACKGROUND: Neutrophil extracellular traps (NETs) play pivotal roles in various pathological processes. The formation of NETs is impaired in acute myeloid leukemia (AML), which can result in immunodeficiency and increased susceptibility to infection.
stDyer enables spatial domain clustering with dynamic graph embedding.
Genome biology
Feb 20, 2025
Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. We introduce stDyer, an end-to-end deep learning framework ...
G2M-checkpoint related immune barrier structure and signature for prognosis and immunotherapy response in hepatocellular carcinoma: insights from spatial transcriptome and machine learning.
Journal of translational medicine
Feb 18, 2025
BACKGROUND: Hepatocellular carcinoma (HCC) treatment remains challenging, particularly for immune checkpoint inhibitors (ICIs) non-response patients. Spatial transcriptome (ST) data and machine learning algorithms offer new insights into understandin...
Identification of biomarkers in Alzheimer's disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms.
PloS one
Feb 18, 2025
BACKGROUND: Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer's disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. The...
Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic models.
Scientific reports
Feb 16, 2025
The objective of this study was to employ machine learning to identify shared differentially expressed genes (DEGs) in prostate cancer (PCa) initiation and castration resistance, aiming to establish a robust prognostic model and enhance understanding...
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