AIMC Topic: Sequence Analysis, RNA

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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...
Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Humans Machine Learning Neoplasms Gene Expression Profiling Transcriptome 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...
Sequence Analysis, RNA Species Specificity Transcriptome Deep Learning Single-Cell Analysis RNA-Seq Mice Humans Animals Gene Expression Profiling
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DHUpredET: A comparative computational approach for identification of dihydrouridine modification sites in RNA sequence.

Analytical biochemistry Mar 6, 2025
Laboratory-based detection of D sites is laborious and expensive. In this study, we developed effective machine learning models employing efficient feature encoding methods to identify D sites. Initially, we explored various state-of-the-art feature ...
RNA Base Sequence Uridine Machine Learning Computational Biology Sequence Analysis, RNA
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EVlncRNA-net: A dual-channel deep learning approach for accurate prediction of experimentally validated lncRNAs.

International journal of biological macromolecules Mar 6, 2025
Long non-coding RNAs (lncRNAs) play key roles in numerous biological processes and are associated with various human diseases. High-throughput RNA sequencing (HTlncRNAs) has identified tens of thousands of lncRNAs across species, but only a small fra...
Humans Neural Networks, Computer Animals Sequence Analysis, RNA RNA, Long Noncoding High-Throughput Nucleotide Sequencing Deep Learning Mice Computational Biology
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scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 5, 2025
Single-cell RNA sequencing (scRNA-seq) has unveiled extensive cellular heterogeneity, yet precise cell type annotation and the identification of novel cell populations remain significant challenges. scHeteroNet, a novel graph neural network framework...
Single-Cell Analysis Humans Sequence Analysis, RNA Neural Networks, Computer Graph Neural Networks
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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 Single-Cell Gene Expression Analysis Single-Cell Analysis Sequence Analysis, RNA Animals RNA-Seq Gene Regulatory Networks
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A novel coarsened graph learning method for scalable single-cell data analysis.

Computers in biology and medicine Feb 24, 2025
The emergence of single-cell technologies, including flow and mass cytometry, as well as single-cell RNA sequencing, has revolutionized the study of cellular heterogeneity, generating vast datasets rich in biological insights. Despite the effectivene...
Single-Cell Analysis Computational Biology Sequence Analysis, RNA Humans Machine Learning Neural Networks, Computer Algorithms
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Integration of single-cell and bulk RNA sequencing data using machine learning identifies oxidative stress-related genes LUM and PCOLCE2 as potential biomarkers for heart failure.

International journal of biological macromolecules Feb 8, 2025
Oxidative stress (OS) is a pivotal mechanism driving the progression of cardiovascular diseases, particularly heart failure (HF). However, the comprehensive characterisation of OS-related genes in HF remains largely unexplored. In the present study, ...
Extracellular Matrix Proteins Lumican Sequence Analysis, RNA Disease Models, Animal Heart Failure Single-Cell Analysis Animals Mice Male Oxidative Stress Humans Machine Learning Biomarkers
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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...
Humans Algorithms Gene Expression Profiling RNA-Seq Deep Learning Sequence Analysis, RNA Single-Cell Analysis Cluster Analysis
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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...
Computational Biology Deep Learning Cluster Analysis Sequence Analysis, RNA Humans Single-Cell Analysis Gene Expression Profiling RNA-Seq
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