AIMC Topic: Single-Cell Gene Expression 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...
Neural Networks, Computer Algorithms Humans Single-Cell Gene Expression Analysis Computational Biology RNA-Seq Deep Learning
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scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.

Briefings in bioinformatics Jan 22, 2024
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by s...
Deep Learning Biomedical Research Molecular Sequence Annotation Single-Cell Gene Expression Analysis Humans Disease Progression
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GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Briefings in bioinformatics Sep 22, 2023
Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq ...
Single-Cell Gene Expression Analysis Gene Ontology Genomics Algorithms Deep Learning
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scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning.

Briefings in bioinformatics Sep 20, 2023
Annotation of cell-types is a critical step in the analysis of single-cell RNA sequencing (scRNA-seq) data that allows the study of heterogeneity across multiple cell populations. Currently, this is most commonly done using unsupervised clustering al...
Gene Expression Profiling Deep Learning Algorithms Sequence Analysis, RNA Cluster Analysis Single-Cell Gene Expression Analysis
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A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection.

Briefings in bioinformatics May 19, 2023
Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurement...
Unsupervised Machine Learning Single-Cell Gene Expression Analysis Single-Cell Analysis Algorithms Cluster Analysis Gene Expression Profiling Sequence Analysis, RNA
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Single-cell gene regulatory network prediction by explainable AI.

Nucleic acids research Feb 28, 2023
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding ...
Gene Regulatory Networks Lung Neoplasms Single-Cell Gene Expression Analysis Neoplasms Deep Learning Gene Expression Regulation Humans
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HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

Bioinformatics (Oxford, England) Feb 7, 2022
MOTIVATION: With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among differ...
Deep Learning Single-Cell Analysis Gene Expression Profiling Sequence Analysis, RNA Single-Cell Gene Expression Analysis Exome Sequencing
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scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.

Bioinformatics (Oxford, England) Jan 12, 2022
MOTIVATION: Single-cell RNA-seq (scRNA-seq) has been widely used to resolve cellular heterogeneity. After collecting scRNA-seq data, the natural next step is to integrate the accumulated data to achieve a common ontology of cell types and states. Thu...
Single-Cell Analysis Gene Expression Profiling Deep Learning Software Sequence Analysis, RNA Single-Cell Gene Expression Analysis
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