AIMC Topic: RNA-Seq

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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...
Gene Regulatory Networks Single-Cell Gene Expression Analysis Computational Biology Sequence Analysis, RNA Chromatin Immunoprecipitation Sequencing Graph Neural Networks Neural Networks, Computer Single-Cell Analysis RNA-Seq Humans
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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...
Algorithms Animals Deep Learning Humans Neural Networks, Computer Software Mice Computational Biology Sequence Analysis, RNA Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis
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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...
Humans Algorithms Gene Expression Profiling Computational Biology Single-Cell Analysis RNA-Seq Sequence Analysis, RNA Single-Cell Gene Expression Analysis 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, ...
Computational Biology Sequence Analysis, RNA RNA-Seq Single-Cell Gene Expression Analysis Humans Algorithms Gene Expression Profiling Transcriptome Deep Learning Single-Cell Analysis
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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 Neural Networks, Computer Algorithms Animals Single-Cell Analysis RNA-Seq Single-Cell Gene Expression Analysis Humans
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m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

Briefings in bioinformatics Sep 23, 2024
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing ...
Software Transcriptome Deep Learning Humans Sequence Analysis, RNA Epigenesis, Genetic Adenosine RNA-Seq Computational Biology RNA, Messenger Nanopores Nanopore Sequencing
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DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Briefings in bioinformatics May 23, 2024
Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. Howe...
Sequence Analysis, RNA Transcription Factors RNA-Seq Humans Gene Regulatory Networks Deep Learning Computational Biology Single-Cell Analysis
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Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Briefings in bioinformatics May 23, 2024
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analys...
Humans RNA-Seq Neural Networks, Computer Single-Cell Gene Expression Analysis Algorithms Computational Biology Deep Learning
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AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature.

Briefings in bioinformatics May 23, 2024
Neurodegenerative diseases, such as Alzheimer's disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimer's Identification Tool (AITeQ) using ribonucleic acid-seq...
RNA-Seq Computational Biology Alzheimer Disease Algorithms Gene Expression Profiling Humans Transcriptome Machine Learning
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GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2024
A generative adversarial network (GAN) is a generative model that consists of two adversarial networks, a discriminator and a generator, usually in the form of neural networks. One of the useful things about applying GANs is that they can synthesize ...
RNA-Seq Machine Learning Humans Computational Biology Algorithms Neural Networks, Computer Sequence Analysis, RNA Disease Progression
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