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Sequence Analysis, RNA

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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 39602449
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 ...
Cellular Senescence Female Gene Expression Profiling Graft Rejection Graft Survival Humans Kidney Transplantation Machine Learning Male Multiomics Sequence Analysis, RNA Single-Cell Analysis
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Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.

Briefings in bioinformatics 39550222
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomi...
Algorithms Computational Biology Gene Expression Profiling Humans RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis Supervised Machine Learning
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scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning.

Briefings in bioinformatics 39708840
Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. How...
Algorithms Animals Computational Biology Deep Learning Humans Mice Neural Networks, Computer RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis Software
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Deep Learning and Single-Cell Sequencing Analyses Unveiling Key Molecular Features in the Progression of Carotid Atherosclerotic Plaque.

Journal of cellular and molecular medicine 39586797
Rupture of advanced carotid atherosclerotic plaques increases the risk of ischaemic stroke, which has significant global morbidity and mortality rates. However, the specific characteristics of immune cells with dysregulated function and proven biomar...
Aged Biomarkers Carotid Artery Diseases Deep Learning Disease Progression Female Gene Expression Profiling Humans Male Middle Aged Monocytes Plaque, Atherosclerotic Sequence Analysis, RNA Single-Cell Analysis
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Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.

Nature methods 39543284
The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process ...
Animals Blastocyst Cell Lineage Deep Learning Embryonic Development Gene Expression Profiling Humans Mice Pluripotent Stem Cells Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Scientific reports 39732739
Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3-5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel rol...
Animals Biomarkers Deep Learning Mice Mice, Inbred C57BL Sequence Analysis, RNA Single-Cell Analysis
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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 39670822
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 ...
Algorithms Cell Differentiation Cluster Analysis Computational Biology Computer Simulation Fuzzy Logic Gene Expression Profiling Humans Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking.

Renal failure 39632251
Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) ...
Adult Allografts Cell Death Female Fibrosis Gene Expression Profiling Graft Rejection Graft Survival Humans Kidney Kidney Transplantation Machine Learning Male Middle Aged Molecular Docking Simulation Sequence Analysis, RNA Transcriptome
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Machine learning-optimized targeted detection of alternative splicing.

Nucleic acids research 39727154
RNA sequencing (RNA-seq) is widely adopted for transcriptome analysis but has inherent biases that hinder the comprehensive detection and quantification of alternative splicing. To address this, we present an efficient targeted RNA-seq method that gr...
Algorithms Alternative Splicing High-Throughput Nucleotide Sequencing Humans Machine Learning RNA-Seq Sequence Analysis, RNA
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KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.

Journal of genetics and genomics = Yi chuan xue bao 39577768
KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics (ST) data. ST technologies provide insights into gene...
Animals Computational Biology Deep Learning Gene Expression Profiling Humans Mice Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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