AIMC Topic: RNA-Seq

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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 ...
Humans Disease Progression RNA-Seq Computational Biology Sequence Analysis, RNA Neural Networks, Computer Machine Learning Algorithms
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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...
Mice Leukocytes, Mononuclear Deep Learning Sequence Analysis, RNA Gene Expression Profiling Animals Single-Cell Analysis RNA-Seq
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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...
Single-Cell Analysis RNA-Seq Sequence Analysis, RNA Cluster Analysis Neural Networks, Computer Gene Expression Profiling
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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 ...
Cluster Analysis Gene Expression Regulation Gene Expression Profiling Deep Learning Single-Cell Analysis Sequence Analysis, RNA Transcription Factors RNA-Seq Gene Regulatory Networks Humans Neural Networks, Computer Algorithms
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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...
Machine Learning Algorithms Gene Expression Profiling Humans RNA-Seq Sequence Analysis, RNA Cluster Analysis Transcriptome Deep Learning Single-Cell Analysis
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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 Sequence Analysis, RNA Deep Learning Single-Cell Analysis RNA-Seq
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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...
Single-Cell Analysis Supervised Machine Learning Saccharomyces cerevisiae Humans RNA-Seq Computer Simulation Mice Escherichia coli Neural Networks, Computer Gene Regulatory Networks Databases, Nucleic Acid Animals Computational Biology Bias Algorithms 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 HEK293 Cells Methyltransferases Animals Mouse Embryonic Stem Cells Humans Nucleotide Motifs RNA Processing, Post-Transcriptional RNA-Seq Machine Learning Sensitivity and Specificity Mice Adenosine
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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...
Epistasis, Genetic Animals Gene Expression Profiling Deep Learning Humans Algorithms Cells, Cultured Gene Regulatory Networks Time Factors Computational Biology Mice Single-Cell Analysis 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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