AIMC Topic: RNA-Seq

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scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

BMC genomics Jul 12, 2022
BACKGROUND: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for e...
Gene Expression Profiling Sequence Analysis, RNA Discriminant Analysis RNA Deep Learning Single-Cell Analysis RNA-Seq
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One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.

Genome biology Apr 20, 2022
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-se...
Cluster Analysis Exome Sequencing Single-Cell Analysis RNA-Seq Sequence Analysis, RNA Machine Learning Algorithms Gene Expression Profiling
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A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

PLoS computational biology Mar 10, 2022
Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limite...
RNA-Seq Cluster Analysis Exome Sequencing Artificial Intelligence Single-Cell Analysis
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Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment.

Oncoimmunology Feb 25, 2022
The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterogeneity is the key for developing immunotherapy. However, the available methods can only partially po...
Exome Sequencing Deep Learning RNA-Seq Humans Immunotherapy Tumor Microenvironment
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Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

International journal of molecular sciences Feb 24, 2022
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individual...
RNA-Seq Gene Ontology SARS-CoV-2 Gene Expression Profiling Transcriptome Algorithms Reproducibility of Results Humans Neural Networks, Computer Machine Learning COVID-19
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Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Nature communications Feb 3, 2022
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic...
Male Transcriptome Deep Learning Aged Aged, 80 and over RNA, Messenger Proteomics Proteome Cognitive Dysfunction Dementia RNA-Seq Humans Neural Networks, Computer Brain Female Protein Biosynthesis Mass Spectrometry Alzheimer Disease
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A neural network-based method for exhaustive cell label assignment using single cell RNA-seq data.

Scientific reports Jan 18, 2022
The fast-advancing single cell RNA sequencing (scRNA-seq) technology enables researchers to study the transcriptome of heterogeneous tissues at a single cell level. The initial important step of analyzing scRNA-seq data is usually to accurately annot...
Sequence Analysis, RNA Single-Cell Analysis Humans Animals RNA-Seq Neural Networks, Computer Gene Expression Profiling Transcriptome
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Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.

Scientific reports Jan 7, 2022
Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individu...
RNA-Seq Mice Databases, Genetic Cluster Analysis Cell Line Gene Expression Regulation Machine Learning RNA Humans Animals Single-Cell Analysis
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PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Genomics Jan 6, 2022
Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer researc...
Gene Expression Humans Machine Learning Gene Expression Profiling RNA-Seq Software Sequence Analysis, RNA Algorithms Neoplasms
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DEGnext: classification of differentially expressed genes from RNA-seq data using a convolutional neural network with transfer learning.

BMC bioinformatics Jan 6, 2022
BACKGROUND: A limitation of traditional differential expression analysis on small datasets involves the possibility of false positives and false negatives due to sample variation. Considering the recent advances in deep learning (DL) based models, we...
RNA-Seq Support Vector Machine Neural Networks, Computer Machine Learning Neoplasms Humans
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