AIMC Topic: RNA-Seq

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Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis.

BMC cancer Apr 9, 2025
BACKGROUND: Gastric cancer (GC) is a highly aggressive and heterogeneous cancer with extremely complex biological characteristics. Lysine and its metabolism are closely related to human cancer, but little is known about how lysine metabolism-related ...
Prognosis Stomach Neoplasms Humans Machine Learning Sequence Analysis, RNA Vimentin Biomarkers, Tumor Single-Cell Gene Expression Analysis RNA-Seq Cell Line, Tumor Gene Expression Regulation, Neoplastic Computational Biology Lysine Gene Regulatory Networks Single-Cell Analysis Nomograms Cell Proliferation Carcinogenesis
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Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.

JCI insight Apr 8, 2025
Using transcriptomic profiling at single-cell resolution, we investigated cell-intrinsic and cell-extrinsic signatures associated with pathogenesis and inflammation-driven fibrosis in both adult and pediatric patients with localized scleroderma (LS)....
Scleroderma, Systemic Scleroderma, Localized Gene Expression Profiling Transcriptome Case-Control Studies Single-Cell Analysis RNA-Seq Adolescent Child Adult Male Middle Aged Humans Machine Learning Female
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scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

BMC genomics Apr 7, 2025
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First...
Software Algorithms Deep Learning Single-Cell Analysis Humans Sequence Analysis, RNA Cluster Analysis Autoencoder RNA-Seq Single-Cell Gene Expression Analysis Computational Biology
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Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research.

Cancer letters Apr 3, 2025
In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification o...
Humans Computational Biology Gene Expression Profiling Gene Expression Regulation, Neoplastic Transcriptome Single-Cell Analysis Tumor Microenvironment RNA-Seq Biomarkers, Tumor Neoplasms
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Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment Mar 31, 2025
Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor st...
Gene Expression Regulation, Plant Neural Networks, Computer Droughts Transcription Factors Chromatin Immunoprecipitation Sequencing RNA-Seq Oryza Data Mining Gene Regulatory Networks Plant Proteins Stress, Physiological
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scCorrect: Cross-modality label transfer from scRNA-seq to scATAC-seq using domain adaptation.

Analytical biochemistry Mar 27, 2025
Cell type annotation in single-cell chromatin accessibility sequencing (scATAC-seq) is crucial for enabling researchers to identify subpopulations of cells associated with specific diseases, elucidate gene regulatory networks, and discover markers in...
Humans RNA-Seq Neural Networks, Computer Sequence Analysis, RNA Single-Cell Gene Expression Analysis Algorithms Chromatin Single-Cell Analysis
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Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

PLoS genetics Mar 19, 2025
Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from complex transcriptome datasets. Geneformer, a pre-trained model using a Transformer Encoder archite...
Humans Animals Gene Expression Profiling Mice Deep Learning Transcriptome Species Specificity Sequence Analysis, RNA Single-Cell Analysis RNA-Seq
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Investigation of cell development and tissue structure network based on natural Language processing of scRNA-seq data.

Journal of translational medicine Mar 4, 2025
BACKGROUND: Single-cell multi-omics technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our understanding of cellular heterogeneity and development by providing insights into gene expression at the single-cell level...
Natural Language Processing Cell Differentiation Organ Specificity Humans Gene Regulatory Networks Single-Cell Gene Expression Analysis Sequence Analysis, RNA Animals Single-Cell Analysis RNA-Seq
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Deep learning powered single-cell clustering framework with enhanced accuracy and stability.

Scientific reports Feb 3, 2025
Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of cellular diversity research. Unsupervised clustering, a key technique in this exploration, allows for the identification of distinct cell types within a population. Graph-based de...
RNA-Seq Humans Sequence Analysis, RNA Cluster Analysis Deep Learning Algorithms Single-Cell Analysis Gene Expression Profiling
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scSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.

BMC bioinformatics Jan 29, 2025
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology...
Sequence Analysis, RNA Cluster Analysis Single-Cell Analysis Humans RNA-Seq Gene Expression Profiling Computational Biology Deep Learning
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