AIMC Topic: Single-Cell Analysis

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Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics.

Briefings in bioinformatics Nov 22, 2024
Recent advances in single-cell RNA-Sequencing (scRNA-Seq) technologies have revolutionized our ability to gather molecular insights into different phenotypes at the level of individual cells. The analysis of the resulting data poses significant chall...
Phenotype SARS-CoV-2 Gene Expression Profiling Lupus Erythematosus, Systemic Neural Networks, Computer Single-Cell Analysis Transcriptome Alzheimer Disease Humans COVID-19 Deep Learning Sequence Analysis, RNA Algorithms
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Deep learning in integrating spatial transcriptomics with other modalities.

Briefings in bioinformatics Nov 22, 2024
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histolog...
Deep Learning Single-Cell Analysis Computational Biology Animals Humans Gene Expression Profiling Transcriptome
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Integrating scRNA-seq and scATAC-seq with inter-type attention heterogeneous graph neural networks.

Briefings in bioinformatics Nov 22, 2024
Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for decipherin...
Sequence Analysis, RNA Chromatin Immunoprecipitation Sequencing Graph Neural Networks Gene Regulatory Networks Single-Cell Gene Expression Analysis Computational Biology Single-Cell Analysis RNA-Seq Humans Neural Networks, Computer
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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 Nov 22, 2024
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...
Computational Biology Sequence Analysis, RNA RNA-Seq Single-Cell Gene Expression Analysis Mice Humans Neural Networks, Computer Software Deep Learning Single-Cell Analysis Algorithms Animals
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BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.

Briefings in bioinformatics Nov 22, 2024
Studying the outcomes of genetic perturbation based on single-cell RNA-seq data is crucial for understanding genetic regulation of cells. However, the high cost of cellular experiments and single-cell sequencing restrict us from measuring the full co...
Algorithms Single-Cell Analysis Gene Regulatory Networks Computational Biology Humans Neural Networks, Computer
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Unveiling Varied Cell Death Patterns in Lung Adenocarcinoma Prognosis and Immunotherapy Based on Single-Cell Analysis and Machine Learning.

Journal of cellular and molecular medicine Nov 1, 2024
Programmed cell death (PCD) pathways hold significant influence in the etiology and progression of a variety of cancer forms, particularly offering promising prognostic markers and clues to drug sensitivity for lung adenocarcinoma (LUAD) patients. We...
Humans Tumor Microenvironment Biomarkers, Tumor Male Cell Line, Tumor Machine Learning Prognosis Female Adenocarcinoma of Lung Gene Expression Regulation, Neoplastic Single-Cell Analysis Apoptosis Lung Neoplasms Cell Death Immunotherapy
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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 Nov 1, 2024
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...
Single-Cell Analysis Monocytes Sequence Analysis, RNA Plaque, Atherosclerotic Disease Progression Gene Expression Profiling Deep Learning Humans Male Carotid Artery Diseases Biomarkers Female Middle Aged Aged
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Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.

Briefings in bioinformatics Sep 23, 2024
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...
Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Humans Algorithms Gene Expression Profiling Computational Biology Sequence Analysis, RNA Supervised Machine Learning
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scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.

Briefings in bioinformatics Sep 23, 2024
The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, ...
Single-Cell Gene Expression Analysis Deep Learning Gene Expression Profiling Humans Single-Cell Analysis Computational Biology Transcriptome RNA-Seq Algorithms Sequence Analysis, RNA
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Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.

Briefings in bioinformatics Sep 23, 2024
The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, w...
Computational Biology Sequence Analysis, RNA Animals Humans Single-Cell Analysis RNA-Seq Neural Networks, Computer Single-Cell Gene Expression Analysis Algorithms
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