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Sequence Analysis, RNA

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Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential.

Genome biology 40340952
BACKGROUND: Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understand...
Biomarkers, Tumor Cell-Free Nucleic Acids Humans Machine Learning Neoplasms Sequence Analysis, RNA
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms.

Frontiers in immunology 40276515
BACKGROUND: The bile acid metabolism (BAM) and fatty acid metabolism (FAM) have been implicated in Kawasaki disease (KD), but their precise mechanisms remain unclear. Identifying signature cells and genes related to BAM and FAM could offer a deeper u...
Biomarkers Fatty Acids Gene Expression Profiling Humans Machine Learning Monocytes Mucocutaneous Lymph Node Syndrome Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.

Bioinformatics (Oxford, England) 40234247
MOTIVATION: Several machine learning (ML) algorithms dedicated to the detection of healthy and diseased cell types from single-cell RNA sequencing (scRNA-seq) data have been proposed for biomedical purposes. This raises concerns about their vulnerabi...
Algorithms Gene Expression Profiling Humans Machine Learning Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

International journal of molecular sciences 40332226
Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Nevertheless, the heterogeneity in treatment outcomes necessitates the identification of reliable biom...
Biomarkers, Tumor Breast Neoplasms Cell Death Female Gene Expression Regulation, Neoplastic Humans Machine Learning Middle Aged Neoadjuvant Therapy Prognosis Sequence Analysis, RNA Single-Cell Analysis
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Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

Medicine 40258762
BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechanisms of Calculus Bovis in sepsis using network pharmacology and RNA sequencing.
China Drugs, Chinese Herbal Humans Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Network Pharmacology Protein Interaction Maps Sepsis Sequence Analysis, RNA Single-Cell Analysis
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Transfer learning of multicellular organization via single-cell and spatial transcriptomics.

PLoS computational biology 40258090
Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) m...
Animals Computational Biology Drosophila Gene Expression Profiling Humans Machine Learning Mice Neural Networks, Computer Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.

BMC bioinformatics 40251476
BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug d...
Algorithms Computational Biology Deep Learning Gene Regulatory Networks Humans Machine Learning RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis
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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 40205350
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 ...
Biomarkers, Tumor Carcinogenesis Cell Line, Tumor Cell Proliferation Computational Biology Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Lysine Machine Learning Nomograms Prognosis RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis Stomach Neoplasms Vimentin
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scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.

BMC genomics 40197174
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...
Algorithms Autoencoder Cluster Analysis Computational Biology Deep Learning Humans RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Single-Cell Gene Expression Analysis Software
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Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data.

Briefings in bioinformatics 40315434
Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noi...
Carcinoma, Renal Cell Deep Learning Gene Expression Profiling Humans Neoplasms Phenotype Sequence Analysis, RNA Single-Cell Analysis Transcriptome
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