AIMC Topic: Transcriptome
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Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning.
PLoS computational biology
Apr 3, 2025
Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial tra...
Causal machine learning for single-cell genomics.
Nature genetics
Mar 31, 2025
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transc...
Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.
Frontiers in immunology
Mar 28, 2025
BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associate...
scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures.
Communications biology
Mar 27, 2025
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to dis...
Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke.
Frontiers in immunology
Mar 26, 2025
BACKGROUND: Ischemic stroke (IS) is a major global cause of death and disability, linked to nucleotide metabolism imbalances. This study aimed to identify nucleotide metabolism-related genes associated with IS and explore their roles in disease mecha...
Comprehensive analysis of single-cell and bulk transcriptome unravels immune landscape of atherosclerosis and develops a S100 family based-diagnostic model.
Computational biology and chemistry
Mar 25, 2025
BACKGROUND: The S100 family of calcium-binding proteins (S100s) had been tightly related to the biological processes of various cardiovascular diseases. This study aims to investigate the expression of S100s in Atherosclerosis (AS) and explore their ...
Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning.
PloS one
Mar 25, 2025
BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a substantial economic burden on society.
Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.
European journal of medical research
Mar 22, 2025
BACKGROUND: Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.
Systems biology of Haemonchus contortus - Advancing biotechnology for parasitic nematode control.
Biotechnology advances
Mar 22, 2025
Parasitic nematodes represent a substantial global burden, impacting animal health, agriculture and economies worldwide. Of these worms, Haemonchus contortus - a blood-feeding nematode of ruminants - is a major pathogen and a model for molecular and ...
scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis.
Genome biology
Mar 21, 2025
Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensio...
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