AIMC Topic: RNA-Seq

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BEENE: deep learning-based nonlinear embedding improves batch effect estimation.

Bioinformatics (Oxford, England) Aug 1, 2023
MOTIVATION: Analyzing large-scale single-cell transcriptomic datasets generated using different technologies is challenging due to the presence of batch-specific systematic variations known as batch effects. Since biological and technological differe...
Animals Single-Cell Analysis RNA-Seq Mice Sequence Analysis, RNA Leukocytes, Mononuclear Gene Expression Profiling Deep Learning
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Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.

Briefings in bioinformatics Mar 10, 2022
Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central importance for the analysis of the scRNA-seq data, as it can be used to identify put...
RNA-Seq Sequence Analysis, RNA Cluster Analysis Neural Networks, Computer Gene Expression Profiling Single-Cell Analysis
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SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

Briefings in bioinformatics Jan 17, 2022
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using ...
Gene Expression Profiling Deep Learning Single-Cell Analysis RNA-Seq Humans Neural Networks, Computer Algorithms Cluster Analysis Gene Expression Regulation Gene Regulatory Networks Sequence Analysis, RNA Transcription Factors
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Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Briefings in bioinformatics Jan 17, 2022
Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant incre...
Humans Machine Learning Sequence Analysis, RNA Algorithms Deep Learning Cluster Analysis Gene Expression Profiling Single-Cell Analysis Transcriptome RNA-Seq
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High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning.

Briefings in bioinformatics Jan 17, 2022
Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene spa...
Gene Expression Profiling RNA-Seq Sequence Analysis, RNA Deep Learning Single-Cell Analysis
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GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology Jan 1, 2022
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...
Mice Computational Biology Escherichia coli Saccharomyces cerevisiae Databases, Nucleic Acid Neural Networks, Computer Bias Single-Cell Analysis Algorithms Supervised Machine Learning Humans RNA-Seq Gene Regulatory Networks Computer Simulation Animals Deep Learning
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Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning.

Nucleic acids research Sep 20, 2021
N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based appr...
RNA, Messenger Animals HEK293 Cells Humans Nucleotide Motifs Mouse Embryonic Stem Cells Methyltransferases Machine Learning RNA-Seq RNA Processing, Post-Transcriptional Adenosine Sensitivity and Specificity Mice
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Deep learning of gene relationships from single cell time-course expression data.

Briefings in bioinformatics Sep 2, 2021
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offe...
Animals Epistasis, Genetic Gene Expression Profiling Deep Learning Humans Algorithms Cells, Cultured Time Factors Gene Regulatory Networks Computational Biology Single-Cell Analysis Mice Models, Genetic RNA-Seq
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Deep embedded clustering with multiple objectives on scRNA-seq data.

Briefings in bioinformatics Sep 2, 2021
In recent years, single-cell RNA sequencing (scRNA-seq) technologies have been widely adopted to interrogate gene expression of individual cells; it brings opportunities to understand the underlying processes in a high-throughput manner. Deep embedde...
Single-Cell Analysis RNA-Seq Computational Biology Mutation Gene Ontology Models, Genetic Cluster Analysis Algorithms Reproducibility of Results Gene Expression Profiling Humans Neural Networks, Computer
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Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data.

Briefings in bioinformatics Sep 2, 2021
Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface marker...
Lymphocytes RNA Datasets as Topic RNA-Seq Erythroid Cells Humans Sequence Analysis, RNA Deep Learning Cluster Analysis Single-Cell Analysis Immunophenotyping
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