AIMC Topic: Sequence Analysis, RNA

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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 Computational Biology Sequence Analysis, RNA RNA, Long Noncoding Animals Deep Learning Mice High-Throughput Nucleotide Sequencing
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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...
Graph Neural Networks Neural Networks, Computer Single-Cell Analysis Humans Sequence Analysis, RNA
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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 Single-Cell Gene Expression Analysis Sequence Analysis, RNA Humans Animals Single-Cell Analysis 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...
Humans Neural Networks, Computer Machine Learning Algorithms Sequence Analysis, RNA Single-Cell Analysis Computational Biology
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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, ...
Heart Failure Extracellular Matrix Proteins Lumican Sequence Analysis, RNA Disease Models, Animal Biomarkers Humans Animals Machine Learning Male Oxidative Stress Single-Cell Analysis Mice
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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...
Cluster Analysis RNA-Seq Sequence Analysis, RNA Humans Algorithms Gene Expression Profiling Deep Learning Single-Cell 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 Sequence Analysis, RNA Cluster Analysis Humans Single-Cell Analysis Gene Expression Profiling RNA-Seq
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Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer.

Biochimica et biophysica acta. Molecular basis of disease Jan 25, 2025
Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (sc...
Drug Resistance, Neoplasm Organoids Humans Single-Cell Analysis Pyrimidinones Machine Learning Sequence Analysis, RNA Colorectal Neoplasms Adenomatous Polyposis Coli Pyridones Antineoplastic Agents
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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 Jan 23, 2025
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...
Computational Biology Sequence Analysis, RNA Deep Learning Humans Gene Expression Profiling Single-Cell Analysis Animals Transcriptome Mice
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